subject:(Particle filter)

University of Alberta

1. Ye, Zi. A Discrete-time Particle Filter and Central Limit Theorem.

Degree: MS, Department of Mathematical and Statistical Sciences, 2013, University of Alberta

URL: https://era.library.ualberta.ca/files/bz60cx09v

We introduce two kinds of particle filters, one is weighted particle filter and the other is resampling particle filter. We prove the Strong Law of Large Numbers and Central Limit Theorem for both particle filters. Then, we show that the resampling particle filter is better than the weighted one.

Subjects/Keywords: Central Limit Theorem; Particle Filter

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APA (6^th Edition):

Ye, Z. (2013). A Discrete-time Particle Filter and Central Limit Theorem. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/bz60cx09v

Chicago Manual of Style (16^th Edition):

Ye, Zi. “A Discrete-time Particle Filter and Central Limit Theorem.” 2013. Masters Thesis, University of Alberta. Accessed September 20, 2019. https://era.library.ualberta.ca/files/bz60cx09v.

MLA Handbook (7^th Edition):

Ye, Zi. “A Discrete-time Particle Filter and Central Limit Theorem.” 2013. Web. 20 Sep 2019.

Vancouver:

Ye Z. A Discrete-time Particle Filter and Central Limit Theorem. [Internet] [Masters thesis]. University of Alberta; 2013. [cited 2019 Sep 20]. Available from: https://era.library.ualberta.ca/files/bz60cx09v.

Council of Science Editors:

Ye Z. A Discrete-time Particle Filter and Central Limit Theorem. [Masters Thesis]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/bz60cx09v

California State University – Chico

2. Daley, Patrick. A REAL-TIME EFFICIENT PARTICLE FILTER ALGORITHM FOR REALISTIC TARGET TRACKING IN GAMES .

Degree: 2012, California State University – Chico

URL: http://hdl.handle.net/10211.4/424

► The challenge for a computer game agent in a partially observable targettracking environment is to estimate where the target might be before taking the next… (more)

Subjects/Keywords: computer games particle filter algorithm

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APA (6^th Edition):

Daley, P. (2012). A REAL-TIME EFFICIENT PARTICLE FILTER ALGORITHM FOR REALISTIC TARGET TRACKING IN GAMES . (Thesis). California State University – Chico. Retrieved from http://hdl.handle.net/10211.4/424

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Daley, Patrick. “A REAL-TIME EFFICIENT PARTICLE FILTER ALGORITHM FOR REALISTIC TARGET TRACKING IN GAMES .” 2012. Thesis, California State University – Chico. Accessed September 20, 2019. http://hdl.handle.net/10211.4/424.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Daley, Patrick. “A REAL-TIME EFFICIENT PARTICLE FILTER ALGORITHM FOR REALISTIC TARGET TRACKING IN GAMES .” 2012. Web. 20 Sep 2019.

Vancouver:

Daley P. A REAL-TIME EFFICIENT PARTICLE FILTER ALGORITHM FOR REALISTIC TARGET TRACKING IN GAMES . [Internet] [Thesis]. California State University – Chico; 2012. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/10211.4/424.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Daley P. A REAL-TIME EFFICIENT PARTICLE FILTER ALGORITHM FOR REALISTIC TARGET TRACKING IN GAMES . [Thesis]. California State University – Chico; 2012. Available from: http://hdl.handle.net/10211.4/424

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Barac, Daniel. Localization algorithms for indoor UAVs.

Degree: UASTECH - Autonomous Unmanned Aircraft Systems Technologies, 2011, Linköping UniversityLinköping University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72217

► The increased market for navigation, localization and mapping system has encouraged the research to dig deeper into these new and challenging areas. The remarkable… (more)

Subjects/Keywords: UAV; particle filter; Octomap; localization

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APA (6^th Edition):

Barac, D. (2011). Localization algorithms for indoor UAVs. (Thesis). Linköping UniversityLinköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72217

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Barac, Daniel. “Localization algorithms for indoor UAVs.” 2011. Thesis, Linköping UniversityLinköping University. Accessed September 20, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72217.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Barac, Daniel. “Localization algorithms for indoor UAVs.” 2011. Web. 20 Sep 2019.

Vancouver:

Barac D. Localization algorithms for indoor UAVs. [Internet] [Thesis]. Linköping UniversityLinköping University; 2011. [cited 2019 Sep 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72217.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Barac D. Localization algorithms for indoor UAVs. [Thesis]. Linköping UniversityLinköping University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72217

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

4. Chitchian, M.M. Adapting Particle Filter Algorithms to the GPU Architecture:.

Degree: 2011, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:9b11258e-cd9c-494d-bbb7-3b85c700b30f

► The particle filter is a Bayesian estimation technique based on Monte Carlo simulations. The non-parametric nature of particle filters makes them ideal for non-linear non-Gaussian… (more)

Subjects/Keywords: particle filter; bayesian estimation; CUDA; GPGPU; GPU; bayes filter; non-linear estimation; distributed particle filter; real-time estimation; parallel particle filter

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APA (6^th Edition):

Chitchian, M. M. (2011). Adapting Particle Filter Algorithms to the GPU Architecture:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:9b11258e-cd9c-494d-bbb7-3b85c700b30f

Chicago Manual of Style (16^th Edition):

Chitchian, M M. “Adapting Particle Filter Algorithms to the GPU Architecture:.” 2011. Masters Thesis, Delft University of Technology. Accessed September 20, 2019. http://resolver.tudelft.nl/uuid:9b11258e-cd9c-494d-bbb7-3b85c700b30f.

MLA Handbook (7^th Edition):

Chitchian, M M. “Adapting Particle Filter Algorithms to the GPU Architecture:.” 2011. Web. 20 Sep 2019.

Vancouver:

Chitchian MM. Adapting Particle Filter Algorithms to the GPU Architecture:. [Internet] [Masters thesis]. Delft University of Technology; 2011. [cited 2019 Sep 20]. Available from: http://resolver.tudelft.nl/uuid:9b11258e-cd9c-494d-bbb7-3b85c700b30f.

Council of Science Editors:

Chitchian MM. Adapting Particle Filter Algorithms to the GPU Architecture:. [Masters Thesis]. Delft University of Technology; 2011. Available from: http://resolver.tudelft.nl/uuid:9b11258e-cd9c-494d-bbb7-3b85c700b30f

University of Alberta

5. Wang, Xingpu. Web Based Particle Filters.

Degree: MS, Department of Mathematical and Statistical Sciences, 2015, University of Alberta

URL: https://era.library.ualberta.ca/files/c6969z0831

► In this thesis, we first introduce two basic problems of filter, the nonlinear filtering and model selection problem. We show that both of them can… (more)

Subjects/Keywords: Branching; Resample; Bayes Inference; Particle Filter

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, X. (2015). Web Based Particle Filters. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/c6969z0831

Chicago Manual of Style (16^th Edition):

Wang, Xingpu. “Web Based Particle Filters.” 2015. Masters Thesis, University of Alberta. Accessed September 20, 2019. https://era.library.ualberta.ca/files/c6969z0831.

MLA Handbook (7^th Edition):

Wang, Xingpu. “Web Based Particle Filters.” 2015. Web. 20 Sep 2019.

Vancouver:

Wang X. Web Based Particle Filters. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2019 Sep 20]. Available from: https://era.library.ualberta.ca/files/c6969z0831.

Council of Science Editors:

Wang X. Web Based Particle Filters. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/c6969z0831

University of Alberta

6. Deng,Jing. Modeling and Development of Soft Sensors with Particle Filtering Approach.

Degree: MS, Department of Chemical and Materials Engineering, 2012, University of Alberta

URL: https://era.library.ualberta.ca/files/k643b130g

► Limitations of measurement techniques and increasingly complex chemical process render difficulties in obtaining certain critical process variables. The hardware sensor reading may have an obvious… (more)

Subjects/Keywords: EM algorithm; Particle Filter; Soft Sensor

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Deng,Jing. (2012). Modeling and Development of Soft Sensors with Particle Filtering Approach. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/k643b130g

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

Deng,Jing. “Modeling and Development of Soft Sensors with Particle Filtering Approach.” 2012. Masters Thesis, University of Alberta. Accessed September 20, 2019. https://era.library.ualberta.ca/files/k643b130g.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

Deng,Jing. “Modeling and Development of Soft Sensors with Particle Filtering Approach.” 2012. Web. 20 Sep 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

Deng,Jing. Modeling and Development of Soft Sensors with Particle Filtering Approach. [Internet] [Masters thesis]. University of Alberta; 2012. [cited 2019 Sep 20]. Available from: https://era.library.ualberta.ca/files/k643b130g.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

Deng,Jing. Modeling and Development of Soft Sensors with Particle Filtering Approach. [Masters Thesis]. University of Alberta; 2012. Available from: https://era.library.ualberta.ca/files/k643b130g

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Texas A&M University

7. Noor, Amina. Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering.

Degree: 2012, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860

► This thesis considers the problem of learning the structure of gene regulatory networks using gene expression time series data. A more realistic scenario where the… (more)

Subjects/Keywords: gene network modeling; particle filter; lasso

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APA (6^th Edition):

Noor, A. (2012). Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Noor, Amina. “Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering.” 2012. Thesis, Texas A&M University. Accessed September 20, 2019. http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Noor, Amina. “Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering.” 2012. Web. 20 Sep 2019.

Vancouver:

Noor A. Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Noor A. Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Georgia Tech

8. Leonard, Andrew. Vehicle tracking using ultra-wideband radar.

Degree: MS, Mechanical Engineering, 2016, Georgia Tech

URL: http://hdl.handle.net/1853/56265

► In this thesis a vehicle tracking problem using an ultra-wideband radar sensor is considered. Prior research is heavily focused on specific applications, such as highway… (more)

Subjects/Keywords: Vehicle tracking; Ultra-wideband; Radar; Particle filter

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APA (6^th Edition):

Leonard, A. (2016). Vehicle tracking using ultra-wideband radar. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56265

Chicago Manual of Style (16^th Edition):

Leonard, Andrew. “Vehicle tracking using ultra-wideband radar.” 2016. Masters Thesis, Georgia Tech. Accessed September 20, 2019. http://hdl.handle.net/1853/56265.

MLA Handbook (7^th Edition):

Leonard, Andrew. “Vehicle tracking using ultra-wideband radar.” 2016. Web. 20 Sep 2019.

Vancouver:

Leonard A. Vehicle tracking using ultra-wideband radar. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/1853/56265.

Council of Science Editors:

Leonard A. Vehicle tracking using ultra-wideband radar. [Masters Thesis]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56265

University of Canterbury

9. Gao, Hongzhi. Real Time Human Tracking in Unconstrained Environments.

Degree: Computer Science and Software Engineering, 2011, University of Canterbury

URL: http://hdl.handle.net/10092/5683

► The tabu search particle filter is proposed in this research based on the integration of the modified tabu search metaheuristic optimization and the genetic particle… (more)

▼ The tabu search particle filter is proposed in this research based on the integration of the modified tabu search metaheuristic optimization and the genetic particle filter. Experiments with this algorithm in real time human tracking applications in unconstrained environments show that it is more robust, accurate and faster than a number of other existing metaheuristic filters, including the evolution particle filter, particle swarm filter, simulated annealing filter, path relink filter and scatter search filter. Quantitative evaluation illustrates that even with only ten particles in the system, the proposed tabu search particle filter has a success rate of 93.85% whereas the success rate of other metaheuristic filters ranged from 68.46% to 17.69% under the same conditions. The accuracy of the proposed algorithm (with ten particles in the tracking system) is 2.69 pixels on average, which is over 3.85 times better than the second best metaheuristic filters in accuracy and 18.13 times better than the average accuracy of all other filters. The proposed algorithm is also the fastest among all metaheuristic filters that have been tested. It achieves approximately 50 frames per second, which is 1.5 times faster than the second fastest algorithm and nineteen times faster than the average speed of all other metaheuristic filters. Furthermore, a unique colour sequence model is developed in this research based on a degenerated form of the hidden Markov model. Quantitative evaluations based on rigid object matching experiments illustrate that the successful matching rate is 5.73 times better than the widely used colour histogram. In terms of speed, the proposed algorithm achieves twice the successful matching rate in about three quarters of the processing time consumed by the colour histogram model. Overall, these results suggest that the two proposed algorithms would be useful in many applications due to their efficiently, accuracy and ability to robustly track people and coloured objects.

Subjects/Keywords: computer vision; real-time tracking; particle filter

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Gao, H. (2011). Real Time Human Tracking in Unconstrained Environments. (Thesis). University of Canterbury. Retrieved from http://hdl.handle.net/10092/5683

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Gao, Hongzhi. “Real Time Human Tracking in Unconstrained Environments.” 2011. Thesis, University of Canterbury. Accessed September 20, 2019. http://hdl.handle.net/10092/5683.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Gao, Hongzhi. “Real Time Human Tracking in Unconstrained Environments.” 2011. Web. 20 Sep 2019.

Vancouver:

Gao H. Real Time Human Tracking in Unconstrained Environments. [Internet] [Thesis]. University of Canterbury; 2011. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/10092/5683.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gao H. Real Time Human Tracking in Unconstrained Environments. [Thesis]. University of Canterbury; 2011. Available from: http://hdl.handle.net/10092/5683

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

10. Lin, Yu-Tsen. Improved Particle Filter for Target Tracking in Decentralized Data Fusion System.

Degree: Master, Electrical Engineering, 2009, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906109-030910

► In this thesis, we investigate a decentralized data fusion system with improved particle filters for target tracking. In many application areas, it becomes essential to… (more)

Subjects/Keywords: decentralized data fusion; target tracking; particle filter

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APA (6^th Edition):

Lin, Y. (2009). Improved Particle Filter for Target Tracking in Decentralized Data Fusion System. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906109-030910

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lin, Yu-Tsen. “Improved Particle Filter for Target Tracking in Decentralized Data Fusion System.” 2009. Thesis, NSYSU. Accessed September 20, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906109-030910.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lin, Yu-Tsen. “Improved Particle Filter for Target Tracking in Decentralized Data Fusion System.” 2009. Web. 20 Sep 2019.

Vancouver:

Lin Y. Improved Particle Filter for Target Tracking in Decentralized Data Fusion System. [Internet] [Thesis]. NSYSU; 2009. [cited 2019 Sep 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906109-030910.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin Y. Improved Particle Filter for Target Tracking in Decentralized Data Fusion System. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0906109-030910

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

11. Marathe, Kedar Pramodan. An Adaptive Nested Particle Filter with Advanced Weighting to handle localization and tracking under extreme sensory occlusion.

Degree: MS, Artificial Intelligence, 2014, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/marathe_kedar_p_201412_ms

► Robot localization under extreme occlusion is a common scenario in the real world. Examples include an autonomous car following another car along long stretches of… (more)

Subjects/Keywords: Particle Filter

…time. We further introduce a modification to the traditional Nested Particle Filter that… …typical MCL algorithm, let us look at the Nested MCL algorithm. The Nested Particle Filter or… …NPF” for short, is a Particle Filter modified to not only localize the subject robot (… …that is similar to the one used for localizing roboti . The Nested Particle Filter (NPF… …modified to become recursive for the Nested Particle Filter or ”NPF”. 6 Propagation…

3 more images…

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APA (6^th Edition):

Marathe, K. P. (2014). An Adaptive Nested Particle Filter with Advanced Weighting to handle localization and tracking under extreme sensory occlusion. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/marathe_kedar_p_201412_ms

Chicago Manual of Style (16^th Edition):

Marathe, Kedar Pramodan. “An Adaptive Nested Particle Filter with Advanced Weighting to handle localization and tracking under extreme sensory occlusion.” 2014. Masters Thesis, University of Georgia. Accessed September 20, 2019. http://purl.galileo.usg.edu/uga_etd/marathe_kedar_p_201412_ms.

MLA Handbook (7^th Edition):

Marathe, Kedar Pramodan. “An Adaptive Nested Particle Filter with Advanced Weighting to handle localization and tracking under extreme sensory occlusion.” 2014. Web. 20 Sep 2019.

Vancouver:

Marathe KP. An Adaptive Nested Particle Filter with Advanced Weighting to handle localization and tracking under extreme sensory occlusion. [Internet] [Masters thesis]. University of Georgia; 2014. [cited 2019 Sep 20]. Available from: http://purl.galileo.usg.edu/uga_etd/marathe_kedar_p_201412_ms.

Council of Science Editors:

Marathe KP. An Adaptive Nested Particle Filter with Advanced Weighting to handle localization and tracking under extreme sensory occlusion. [Masters Thesis]. University of Georgia; 2014. Available from: http://purl.galileo.usg.edu/uga_etd/marathe_kedar_p_201412_ms

Linköping University

12. Emilsson, André. Indoor Navigation Using an iPhone.

Degree: Electrical Engineering, 2010, Linköping University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57890

► Indoor navigation could be used in many applications to enhance performance in its specific area. Anything from serious life critical tasks like aiding firefighters… (more)

Subjects/Keywords: Indoor navigation; iPhone; Particle filter; TECHNOLOGY; TEKNIKVETENSKAP

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APA (6^th Edition):

Emilsson, A. (2010). Indoor Navigation Using an iPhone. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57890

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Emilsson, André. “Indoor Navigation Using an iPhone.” 2010. Thesis, Linköping University. Accessed September 20, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57890.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Emilsson, André. “Indoor Navigation Using an iPhone.” 2010. Web. 20 Sep 2019.

Vancouver:

Emilsson A. Indoor Navigation Using an iPhone. [Internet] [Thesis]. Linköping University; 2010. [cited 2019 Sep 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57890.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Emilsson A. Indoor Navigation Using an iPhone. [Thesis]. Linköping University; 2010. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57890

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of New South Wales

13. Chen, Yu. Facial Segmentation Using Boosted Dynamic Mixture Active Shape Model.

Degree: Computer Science & Engineering, 2015, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/54529 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:35191/SOURCE02?view=true

► Considerable research has been done on automatic extraction of salient features from human faces in images over the last 20 years. For many existing systems,… (more)

▼ Considerable research has been done on automatic extraction of salient features from human faces in images over the last 20 years. For many existing systems, a prior face shape model has been utilized to align with unknown faces and locate important facial features, represented by a set of labelled points around the eyes, nose and mouth. Constraints are applied to the shape model to facilitate reliable face alignment. However, these constraints may lead to inaccuracy and failure in alignment due to complex situations that commonly occur in the real world. This thesis is aimed at extending a current face alignment method, namely Active Shape Model (ASM) proposed by Coates et al. (1995). ASM is a robust approach to accommodate non-rigid object segmentation in the field of computer vision and has been widely used to segment and acquire visual information from the world. In this thesis, some novel variations have been applied to classical ASM to improve its performance. More specifically, machine learning techniques and dynamic tracking ir. the model space are successfully combined with ASM to achieve improved segmentation, and a new hierarchical clustering framework is integrated to accommodate multi-view face shape alignment. In the proposed framework, three elements account for the main contributions. Firstly, the accuracy and robustness of the classical Active Shape Model are enhanced by learning nonlinear classifiers through machine learning algorithms. followed by a boosting method to automatically select the best classifier. Secondly, tracking of the motion of facial feature points benefits from dynamic modelling and particle filtering. Finally, a Gaussian mixture model is adopted to learn clusters of views, and parameters are updated by EM algorithm. Beyond the investigations of the framework, the most significant achievement is the idea of incorporating dynamic information in deformable shape models which distinguishes it from conventional template-based segmentation methods. A number of experiments have been conducted to evaluate the performance of the novel method. Different parameters have been tuned in search of optimal solutions. To assess generalizability, various popular public face databases are utilized. Comparison with published results of other segmentation methods have been carried out. Stability is demonstrated by a set of statistical tests. The proposed approach produces distinctive results on most of the major databases, also outperforms other algorithm both in accuracy and robustness. Advisors/Committee Members: Sowmya, Acrot, Computer Science & Engineering, Faculty of Engineering, UNSW.

Subjects/Keywords: Particle Filter; Active Shape Model; Machine Learning

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APA (6^th Edition):

Chen, Y. (2015). Facial Segmentation Using Boosted Dynamic Mixture Active Shape Model. (Masters Thesis). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/54529 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:35191/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Chen, Yu. “Facial Segmentation Using Boosted Dynamic Mixture Active Shape Model.” 2015. Masters Thesis, University of New South Wales. Accessed September 20, 2019. http://handle.unsw.edu.au/1959.4/54529 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:35191/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Chen, Yu. “Facial Segmentation Using Boosted Dynamic Mixture Active Shape Model.” 2015. Web. 20 Sep 2019.

Vancouver:

Chen Y. Facial Segmentation Using Boosted Dynamic Mixture Active Shape Model. [Internet] [Masters thesis]. University of New South Wales; 2015. [cited 2019 Sep 20]. Available from: http://handle.unsw.edu.au/1959.4/54529 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:35191/SOURCE02?view=true.

Council of Science Editors:

Chen Y. Facial Segmentation Using Boosted Dynamic Mixture Active Shape Model. [Masters Thesis]. University of New South Wales; 2015. Available from: http://handle.unsw.edu.au/1959.4/54529 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:35191/SOURCE02?view=true

University of Edinburgh

14. Zhong, Xionghu. Bayesian framework for multiple acoustic source tracking.

Degree: PhD, 2010, University of Edinburgh

URL: http://hdl.handle.net/1842/4752

► Acoustic source (speaker) tracking in the room environment plays an important role in many speech and audio applications such as multimedia, hearing aids and hands-free… (more)

▼ Acoustic source (speaker) tracking in the room environment plays an important role in many speech and audio applications such as multimedia, hearing aids and hands-free speech communication and teleconferencing systems; the position information can be fed into a higher processing stage for high-quality speech acquisition, enhancement of a specific speech signal in the presence of other competing talkers, or keeping a camera focused on the speaker in a video-conferencing scenario. Most of existing systems focus on the single source tracking problem, which assumes one and only one source is active all the time, and the state to be estimated is simply the source position. However, in practical scenarios, multiple speakers may be simultaneously active, and the tracking algorithm should be able to localise each individual source and estimate the number of sources. This thesis contains three contributions towards solutions to multiple acoustic source tracking in a moderate noisy and reverberant environment. The first contribution of this thesis is proposing a time-delay of arrival (TDOA) estimation approach for multiple sources. Although the phase transform (PHAT) weighted generalised cross-correlation (GCC) method has been employed to extract the TDOAs of multiple sources, it is primarily used for a single source scenario and its performance for multiple TDOA estimation has not been comprehensively studied. The proposed approach combines the degenerate unmixing estimation technique (DUET) and GCC method. Since the speech mixtures are assumed window-disjoint orthogonal (WDO) in the time-frequency domain, the spectrograms can be separated by employing DUET, and the GCC method can then be applied to the spectrogram of each individual source. The probabilities of detection and false alarm are also proposed to evaluate the TDOA estimation performance under a series of experimental parameters. Next, considering multiple acoustic sources may appear nonconcurrently, an extended Kalman particle filtering (EKPF) is developed for a special multiple acoustic source tracking problem, namely “nonconcurrent multiple acoustic tracking (NMAT)”. The extended Kalman filter (EKF) is used to approximate the optimum weights, and the subsequent particle filtering (PF) naturally takes the previous position estimates as well as the current TDOA measurements into account. The proposed approach is thus able to lock on the sharp change of the source position quickly, and avoid the tracking-lag in the general sequential importance resampling (SIR) PF. Finally, these investigations are extended into an approach to track the multiple unknown and time-varying number of acoustic sources. The DUET-GCC method is used to obtain the TDOA measurements for multiple sources and a random finite set (RFS) based Rao-blackwellised PF is employed and modified to track the sources. Each particle has a RFS form encapsulating the states of all sources and is capable of addressing source dynamics: source survival, new source appearance and source deactivation. A data…

Subjects/Keywords: 621.382; Bayesian filter; particle filtering; tracking

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zhong, X. (2010). Bayesian framework for multiple acoustic source tracking. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/4752

Chicago Manual of Style (16^th Edition):

Zhong, Xionghu. “Bayesian framework for multiple acoustic source tracking.” 2010. Doctoral Dissertation, University of Edinburgh. Accessed September 20, 2019. http://hdl.handle.net/1842/4752.

MLA Handbook (7^th Edition):

Zhong, Xionghu. “Bayesian framework for multiple acoustic source tracking.” 2010. Web. 20 Sep 2019.

Vancouver:

Zhong X. Bayesian framework for multiple acoustic source tracking. [Internet] [Doctoral dissertation]. University of Edinburgh; 2010. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/1842/4752.

Council of Science Editors:

Zhong X. Bayesian framework for multiple acoustic source tracking. [Doctoral Dissertation]. University of Edinburgh; 2010. Available from: http://hdl.handle.net/1842/4752

University of Texas – Austin

15. Shen, Xiaohu, active 21st century. Bayesian inference methods for next generation DNA sequencing.

Degree: PhD, Electrical and Computer Engineering, 2014, University of Texas – Austin

URL: http://hdl.handle.net/2152/26176

► Recently developed next-generation sequencing systems are capable of rapid and cost-effective DNA sequencing, thus enabling routine sequencing tasks and taking us one step closer to… (more)

▼ Recently developed next-generation sequencing systems are capable of rapid and cost-effective DNA sequencing, thus enabling routine sequencing tasks and taking us one step closer to personalized medicine. To provide a blueprint of a target genome, next-generation sequencing systems typically employ the so called shotgun sequencing strategy and oversample the genome with a library of relatively short overlapping reads. The order of nucleotides in the short reads is determined by processing acquired noisy signals generated by the sequencing platforms, and the overlaps between the reads are exploited to assemble the target long genome. Next-generation sequencing utilizes massively parallel array-based technology to speed up the sequencing and reduce the cost. However, accuracy and lengths of the short reads are yet to surpass those provided by the conventional slower and costlier Sanger sequencing method. In this thesis, we first focus on Illumina's sequencing-by-synthesis platform which relies on reversible terminator chemistry and describe the acquired signal by a Hidden Markov Model. Relying on this model and sequential Monte Carlo methods, we develop a parameter estimation and base calling scheme called ParticleCall. ParticleCall is tested on an experimental data set obtained by sequencing phiX174 bacteriophage using Illumina's Genome Analyzer II. The results show that ParticleCall scheme is significantly more computationally efficient than the best performing unsupervised base calling method currently available, while achieving the same accuracy. Having addressed the problem of base calling of short reads, we turn our attention to genome assembly. Assembly of a genome from acquired short reads is a computationally daunting task even in the scenario where a reference genome exists. Errors and gaps in the reference, and perfect repeat regions in the target, further render the assembly challenging and cause inaccuracies. We formulate reference-guided assembly as the inference problem on a bipartite graph and solve it using a message-passing algorithm. The proposed algorithm can be interpreted as the classical belief propagation scheme under a certain prior. Unlike existing state-of-the-art methods, the proposed algorithm combines the information provided by the reads without needing to know reliability of the short reads (so-called quality scores). Relation of the message-passing algorithm to a provably convergent power iteration scheme is discussed. Results on both simulated and experimental data demonstrate that the proposed message-passing algorithm outperforms commonly used state-of-the-art tools, and it nearly achieves the performance of a genie-aided maximum a posteriori (MAP) scheme. We then consider the reference-free genome assembly problem, i.e., the de novo assembly. Various methods for de novo assembly have been proposed in literature, all of whom are very sensitive to errors in short reads. We develop a novel error-correction method that enables performance improvements of de novo assembly. The new… Advisors/Committee Members: Vikalo, Haris (advisor).

Subjects/Keywords: DNA sequencing; Particle filter; Message passing

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APA (6^th Edition):

Shen, Xiaohu, a. 2. c. (2014). Bayesian inference methods for next generation DNA sequencing. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/26176

Chicago Manual of Style (16^th Edition):

Shen, Xiaohu, active 21st century. “Bayesian inference methods for next generation DNA sequencing.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed September 20, 2019. http://hdl.handle.net/2152/26176.

MLA Handbook (7^th Edition):

Shen, Xiaohu, active 21st century. “Bayesian inference methods for next generation DNA sequencing.” 2014. Web. 20 Sep 2019.

Vancouver:

Shen, Xiaohu a2c. Bayesian inference methods for next generation DNA sequencing. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/2152/26176.

Council of Science Editors:

Shen, Xiaohu a2c. Bayesian inference methods for next generation DNA sequencing. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/26176

16. Särkkä, Simo. Recursive Bayesian Inference on Stochastic Differential Equations.

Degree: 2006, Helsinki University of Technology

URL: http://lib.tkk.fi/Diss/2006/isbn9512281279/

►

This thesis is concerned with recursive Bayesian estimation of non-linear dynamical systems, which can be modeled as discretely observed stochastic differential equations. The recursive real-time… (more)

▼

This thesis is concerned with recursive Bayesian estimation of non-linear dynamical systems, which can be modeled as discretely observed stochastic differential equations. The recursive real-time estimation algorithms for these continuous-discrete filtering problems are traditionally called optimal filters and the algorithms for recursively computing the estimates based on batches of observations are called optimal smoothers. In this thesis, new practical algorithms for approximate and asymptotically optimal continuous-discrete filtering and smoothing are presented. The mathematical approach of this thesis is probabilistic and the estimation algorithms are formulated in terms of Bayesian inference. This means that the unknown parameters, the unknown functions and the physical noise processes are treated as random processes in the same joint probability space. The Bayesian approach provides a consistent way of computing the optimal filtering and smoothing estimates, which are optimal given the model assumptions and a consistent way of analyzing their uncertainties. The formal equations of the optimal Bayesian continuous-discrete filtering and smoothing solutions are well known, but the exact analytical solutions are available only for linear Gaussian models and for a few other restricted special cases. The main contributions of this thesis are to show how the recently developed discrete-time unscented Kalman filter, particle filter, and the corresponding smoothers can be applied in the continuous-discrete setting. The equations for the continuous-time unscented Kalman-Bucy filter are also derived. The estimation performance of the new filters and smoothers is tested using simulated data. Continuous-discrete filtering based solutions are also presented to the problems of tracking an unknown number of targets, estimating the spread of an infectious disease and to prediction of an unknown time series.

Helsinki University of Technology Laboratory of Computational Engineering publications. Report B, ISSN 1457-0404; 54

Advisors/Committee Members: Helsinki University of Technology, Department of Electrical and Communications Engineering, Laboratory of Computational Engineering.

Subjects/Keywords: Bayesian inference; continuous-discrete filtering; unscented Kalman filter; particle filter

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Särkkä, S. (2006). Recursive Bayesian Inference on Stochastic Differential Equations. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2006/isbn9512281279/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Särkkä, Simo. “Recursive Bayesian Inference on Stochastic Differential Equations.” 2006. Thesis, Helsinki University of Technology. Accessed September 20, 2019. http://lib.tkk.fi/Diss/2006/isbn9512281279/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Särkkä, Simo. “Recursive Bayesian Inference on Stochastic Differential Equations.” 2006. Web. 20 Sep 2019.

Vancouver:

Särkkä S. Recursive Bayesian Inference on Stochastic Differential Equations. [Internet] [Thesis]. Helsinki University of Technology; 2006. [cited 2019 Sep 20]. Available from: http://lib.tkk.fi/Diss/2006/isbn9512281279/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Särkkä S. Recursive Bayesian Inference on Stochastic Differential Equations. [Thesis]. Helsinki University of Technology; 2006. Available from: http://lib.tkk.fi/Diss/2006/isbn9512281279/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Alberta

17. Shenoy, Arjun Vsiwanath. Unconstrained nonlinear state estimation for chemical processes.

Degree: MS, Department of Chemical and Materials Engineering, 2010, University of Alberta

URL: https://era.library.ualberta.ca/files/n009w369x

► Estimation theory is a branch of statistics and probability that derives information about random variables based on known information. In process engineering, state estimation is… (more)

Subjects/Keywords: Kalman filter; Particle filter; Estimation theory; polymer reactors

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Shenoy, A. V. (2010). Unconstrained nonlinear state estimation for chemical processes. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/n009w369x

Chicago Manual of Style (16^th Edition):

Shenoy, Arjun Vsiwanath. “Unconstrained nonlinear state estimation for chemical processes.” 2010. Masters Thesis, University of Alberta. Accessed September 20, 2019. https://era.library.ualberta.ca/files/n009w369x.

MLA Handbook (7^th Edition):

Shenoy, Arjun Vsiwanath. “Unconstrained nonlinear state estimation for chemical processes.” 2010. Web. 20 Sep 2019.

Vancouver:

Shenoy AV. Unconstrained nonlinear state estimation for chemical processes. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2019 Sep 20]. Available from: https://era.library.ualberta.ca/files/n009w369x.

Council of Science Editors:

Shenoy AV. Unconstrained nonlinear state estimation for chemical processes. [Masters Thesis]. University of Alberta; 2010. Available from: https://era.library.ualberta.ca/files/n009w369x

18. Tapiero Bernal, Juan Esteban. Bayesian Estimation For Tracking Of Spiraling Reentry Vehicles.

Degree: 2013, Marquette University

URL: https://epublications.marquette.edu/theses_open/203

► This thesis presents a development of a physics-based dynamics model of a spiraling atmospheric reentry vehicle. An analysis of the trajectory characteristics, using elements from… (more)

Subjects/Keywords: Estimation; Extended Kalman filter; Modeling; Particle filter; Reentry; Tracking

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tapiero Bernal, J. E. (2013). Bayesian Estimation For Tracking Of Spiraling Reentry Vehicles. (Thesis). Marquette University. Retrieved from https://epublications.marquette.edu/theses_open/203

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Tapiero Bernal, Juan Esteban. “Bayesian Estimation For Tracking Of Spiraling Reentry Vehicles.” 2013. Thesis, Marquette University. Accessed September 20, 2019. https://epublications.marquette.edu/theses_open/203.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Tapiero Bernal, Juan Esteban. “Bayesian Estimation For Tracking Of Spiraling Reentry Vehicles.” 2013. Web. 20 Sep 2019.

Vancouver:

Tapiero Bernal JE. Bayesian Estimation For Tracking Of Spiraling Reentry Vehicles. [Internet] [Thesis]. Marquette University; 2013. [cited 2019 Sep 20]. Available from: https://epublications.marquette.edu/theses_open/203.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tapiero Bernal JE. Bayesian Estimation For Tracking Of Spiraling Reentry Vehicles. [Thesis]. Marquette University; 2013. Available from: https://epublications.marquette.edu/theses_open/203

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

19. Dean, Adam. Terrain-Based Road Vehicle Localization Using Attitude Measurements.

Degree: PhD, Mechanical Engineering, 2008, Penn State University

URL: https://etda.libraries.psu.edu/catalog/8997

► This research is focused on developing a road vehicle localization technique that is independent of Global Positioning System (GPS) by means of a terrain-based method… (more)

Subjects/Keywords: vehicle; terrain-based; estimation; kalman filter; particle filter; localization

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APA (6^th Edition):

Dean, A. (2008). Terrain-Based Road Vehicle Localization Using Attitude Measurements. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/8997

Chicago Manual of Style (16^th Edition):

Dean, Adam. “Terrain-Based Road Vehicle Localization Using Attitude Measurements.” 2008. Doctoral Dissertation, Penn State University. Accessed September 20, 2019. https://etda.libraries.psu.edu/catalog/8997.

MLA Handbook (7^th Edition):

Dean, Adam. “Terrain-Based Road Vehicle Localization Using Attitude Measurements.” 2008. Web. 20 Sep 2019.

Vancouver:

Dean A. Terrain-Based Road Vehicle Localization Using Attitude Measurements. [Internet] [Doctoral dissertation]. Penn State University; 2008. [cited 2019 Sep 20]. Available from: https://etda.libraries.psu.edu/catalog/8997.

Council of Science Editors:

Dean A. Terrain-Based Road Vehicle Localization Using Attitude Measurements. [Doctoral Dissertation]. Penn State University; 2008. Available from: https://etda.libraries.psu.edu/catalog/8997

University of Tennessee – Knoxville

20. Collins, John Parnell. Comparison of Methods for Estimating Stochastic Volatility.

Degree: MS, Mathematics, 2013, University of Tennessee – Knoxville

URL: https://trace.tennessee.edu/utk_gradthes/2403

► Understanding the ever changing stock market has long been of interest to both academic and financial institutions. The early attempts to model the dynamics… (more)

Subjects/Keywords: Particle Filter; Gibbs Sampling; Kalman Filter; Other Applied Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Collins, J. P. (2013). Comparison of Methods for Estimating Stochastic Volatility. (Thesis). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_gradthes/2403

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Collins, John Parnell. “Comparison of Methods for Estimating Stochastic Volatility.” 2013. Thesis, University of Tennessee – Knoxville. Accessed September 20, 2019. https://trace.tennessee.edu/utk_gradthes/2403.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Collins, John Parnell. “Comparison of Methods for Estimating Stochastic Volatility.” 2013. Web. 20 Sep 2019.

Vancouver:

Collins JP. Comparison of Methods for Estimating Stochastic Volatility. [Internet] [Thesis]. University of Tennessee – Knoxville; 2013. [cited 2019 Sep 20]. Available from: https://trace.tennessee.edu/utk_gradthes/2403.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Collins JP. Comparison of Methods for Estimating Stochastic Volatility. [Thesis]. University of Tennessee – Knoxville; 2013. Available from: https://trace.tennessee.edu/utk_gradthes/2403

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Virginia Commonwealth University

21. Conte, Armond S, II. Censoring and Fusion in Non-linear Distributed Tracking Systems with Application to 2D Radar.

Degree: MS, Engineering, 2015, Virginia Commonwealth University

URL: https://scholarscompass.vcu.edu/etd/4068

► The objective of this research is to study various methods for censoring state estimate updates generated from radar measurements. The generated 2-D radar data… (more)

Subjects/Keywords: radar; fusion; censoring; tracking; particle filter; kalman filter; Signal Processing

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APA (6^th Edition):

Conte, Armond S, I. (2015). Censoring and Fusion in Non-linear Distributed Tracking Systems with Application to 2D Radar. (Thesis). Virginia Commonwealth University. Retrieved from https://scholarscompass.vcu.edu/etd/4068

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Conte, Armond S, II. “Censoring and Fusion in Non-linear Distributed Tracking Systems with Application to 2D Radar.” 2015. Thesis, Virginia Commonwealth University. Accessed September 20, 2019. https://scholarscompass.vcu.edu/etd/4068.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Conte, Armond S, II. “Censoring and Fusion in Non-linear Distributed Tracking Systems with Application to 2D Radar.” 2015. Web. 20 Sep 2019.

Vancouver:

Conte, Armond S I. Censoring and Fusion in Non-linear Distributed Tracking Systems with Application to 2D Radar. [Internet] [Thesis]. Virginia Commonwealth University; 2015. [cited 2019 Sep 20]. Available from: https://scholarscompass.vcu.edu/etd/4068.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Conte, Armond S I. Censoring and Fusion in Non-linear Distributed Tracking Systems with Application to 2D Radar. [Thesis]. Virginia Commonwealth University; 2015. Available from: https://scholarscompass.vcu.edu/etd/4068

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

22. Medarametla, Krishna Kalyan. Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter.

Degree: MS, 0133, 2014, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/50584

► In a recent work it has been shown that importance sampling can be avoided in particle filter through an innovation structure inspired by traditional nonlinear… (more)

Subjects/Keywords: Extended Kalman filter; Feedback particle filter; Comparison; Nonlinear filtering

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APA (6^th Edition):

Medarametla, K. K. (2014). Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/50584

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Medarametla, Krishna Kalyan. “Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter.” 2014. Thesis, University of Illinois – Urbana-Champaign. Accessed September 20, 2019. http://hdl.handle.net/2142/50584.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Medarametla, Krishna Kalyan. “Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter.” 2014. Web. 20 Sep 2019.

Vancouver:

Medarametla KK. Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/2142/50584.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Medarametla KK. Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter. [Thesis]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/50584

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of California – Berkeley

23. Lei, Jing. Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results.

Degree: Statistics, 2010, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/3tm9052d

► State space models are powerful modeling tools for stochastic dynamical systems and have been an important research area in the statistics community in the last… (more)

▼ State space models are powerful modeling tools for stochastic dynamical systems and have been an important research area in the statistics community in the last several decades. This thesis makes contributions to the filtering problem, a key inference problem in general state space models. Our work in this area is motivated by both high-dimensional, nonlinear applications such as numerical weather forecasting and fundamental theoretical problems such as the convergence of filters. First we study the ensemble Kalman filters (EnKF), a popular class of filtering methods in geophysics because they are easy to implement in large systems. However, their behavior in non-Gaussian situations is only partially understood. We compare two common versions of EnKF's under non-Gaussianity from a robustness perspective. The results support previous empirical studies on the same issue and provide additional insight in choosing a free parameter in the EnKF algorithms. Second, we consider the filtering problem in high dimensional situations such as numerical weather forecasting. We review the EnKF from a statistical perspective and analyze its sources of bias. Then we propose a new method to reduces the bias, namely the non-linear ensemble adjustment filter (NLEAF). The one-step consistency of the NLEAF is studied and the performance is examined through simulations in two common testbeds in the weather forecasting literature. Finally we look at the theoretical properties of another popular class of filtering methods, the sequential Monte Carlo (SMC) filter. The convergence of SMC filters has been a challenging problem in both probability and statistics. The previous results either depend on strong mixing conditions which only hold in compact spaces or provide no rates of convergence or are under weak notions of distance, limiting the application of their practical use. We provide checkable sufficient conditions under which explicit rates of convergence of the SMC filter can be derived. The conditions essentially requires the regularity of the tail behavior of the process and they are general enough to include a wide class of autoregressive models as well as Gaussian linear models.

Subjects/Keywords: Statistics; chaotic dynamical systems; high dimension data; Kalman filter; particle filter; robustness; state space models

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APA (6^th Edition):

Lei, J. (2010). Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/3tm9052d

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lei, Jing. “Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results.” 2010. Thesis, University of California – Berkeley. Accessed September 20, 2019. http://www.escholarship.org/uc/item/3tm9052d.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lei, Jing. “Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results.” 2010. Web. 20 Sep 2019.

Vancouver:

Lei J. Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results. [Internet] [Thesis]. University of California – Berkeley; 2010. [cited 2019 Sep 20]. Available from: http://www.escholarship.org/uc/item/3tm9052d.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lei J. Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results. [Thesis]. University of California – Berkeley; 2010. Available from: http://www.escholarship.org/uc/item/3tm9052d

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

24. Rosander, Peter. Camera Based Terrain Navigation.

Degree: Electrical Engineering, 2009, Linköping UniversityLinköping University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16953

►

The standard way for both ground and aerial vehicles to navigate is to use anInertial Navigation System, INS, containing an Inertial Measurement Unit, IMU,measuring… (more)

▼

The standard way for both ground and aerial vehicles to navigate is to use anInertial Navigation System, INS, containing an Inertial Measurement Unit, IMU,measuring the acceleration and angular rate, and a GPS measuring the position.The IMU provides high dynamic measurements of the acceleration and the angularrate, which the INS integrates to velocity, position and attitude, respectively.While being completely impossible to jam, the dead-reckoned estimates will driftaway, i.e., the errors are unbounded. In conjunction with a GPS, providing lowdynamic updates with bounded errors, a highly dynamic system without any driftis attained. The weakness of this system is its integrity, since the GPS is easilyjammed with simple equipment and powered only by a small standard battery.When the GPS is jammed this system falls back into the behavior of the INS withunbounded errors. To counter this integrity problem a camera can be used aseither a back up to the GPS or as its replacement. The camera provides imageswhich are then matched versus a reference, e.g., a map or an aerial photo, to getsimilar estimates as the GPS would provide. The camera can of course also bejammed by blocking the view of the camera with smoke. Bad visibility can alsooccur due to bad weather, but a camera based navigation system will definitelybe more robust than one using GPS.This thesis presents two ways to fuse the measurements from the camera and theIMU, both of them utilizing the Harris corner detector to find point correspondencesbetween the camera image and an aerial photo. The systems are evaluatedby simulated data mimicking both a low and a high accuracy IMU and a camerataking snapshots of the aerial photo. Results show that for the simulated cameraimages the implemented corner detector works fine and that the overall result iscomparable to using a GPS.

Standardsättet för både flygande och markgående fordon att navigera är att användaett tröghetsnavigeringssystem, innehållande en IMU som mäter acceleration ochvinkelhastighet, tillsammans med GPS. IMU:n tillhandahåller högfrekventa mätningarav acceleration och vinkelhastighet som integreras till hastighet, positionoch attityd. Ett sådant system är omöjligt att störa, men lider av att de dödräknadestorheterna hastighet, position och attityd, med tiden, kommer att driva ivägifrån de sanna värdena. Tillsammans med GPS, som ger lågfrekventa mätningarav positionen, erhålls ett system med god dynamik och utan drift. Svagheten i ettvvisådant system är dess integritet, då GPS enkelt kan störas med enkel och billigutrustning. För att lösa integritetsproblemet kan en kamera användas, antingensom stöd eller som ersättare till GPS. Kameran tar bilder som matchas gentemoten referens ex. en karta eller ett ortofoto. Det ger liknande mätningar som de GPSger. Ett kamerabaserat system kan visserligen också störas genom att blockerasynfältet för kameran med exempelvis rök. Dålig sikt kan också uppkomma pågrund av dåligt väder eller dimma, men ett kamerabaserat system kommer definitivtatt vara robustare…

Subjects/Keywords: UAV; Kalman Filter; Particle Filter; Image Processing; Estimation; Automatic control; Reglerteknik; Signal processing; Signalbehandling

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APA (6^th Edition):

Rosander, P. (2009). Camera Based Terrain Navigation. (Thesis). Linköping UniversityLinköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16953

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Rosander, Peter. “Camera Based Terrain Navigation.” 2009. Thesis, Linköping UniversityLinköping University. Accessed September 20, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16953.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Rosander, Peter. “Camera Based Terrain Navigation.” 2009. Web. 20 Sep 2019.

Vancouver:

Rosander P. Camera Based Terrain Navigation. [Internet] [Thesis]. Linköping UniversityLinköping University; 2009. [cited 2019 Sep 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16953.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rosander P. Camera Based Terrain Navigation. [Thesis]. Linköping UniversityLinköping University; 2009. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16953

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

George Mason University

25. Palvai, Anoop Kumar. Passive RF Localization Based on RSSI Using Non-linear Bayesian Estimation .

Degree: 2009, George Mason University

URL: http://hdl.handle.net/1920/3407

► RF localization has gained prominence because of its potential for supporting various position based applications. Passive RF Localization based on Received Signal Strength Indicator (RSSI)… (more)

▼ RF localization has gained prominence because of its potential for supporting various position based applications. Passive RF Localization based on Received Signal Strength Indicator (RSSI) uses the strength of received signal from a target by passive listening to infer the range, which is subsequently used for position estimation. The thesis undertakes a study of localization techniques and addresses the problem of accuracy of position estimation. State space model developed for localization is nonlinear and hence does not have a closed form solution. Posterior density for state vector has been derived and simulated using a variant of Kalman Filter and Monte Carlo methods to obtain respective sub-optimal solutions. Least Squared Error approach tries to obtain an estimate that minimizes the squared error whereas and does not reveal any statistical information about the target location. Extended Kalman filter approach tries to estimate the posterior density of target employing approximations of Gaussian state probability distribution and linear state space model and observed to provide better results compared to that of Least Squared Error approach. As the localization model is nonlinear, Extended Kalman filter approximates it with a linear one by employing Taylor series approximation and if the nonlinearity is severe the accuracy of the algorithm suffers. Particle filter approach also tries to estimate the state posterior density with no restrictions and hence is applicable for any generalized system. In this approach probability density function is approximated using a weighted set of particles drawn using Monte Carlo methods and will enable in computing the all the moments of distribution. Recursive Least Squared Error, Extended Kalman and Sampling Importance Resampling Particle Filtering algorithms are designed for localization and their performances are compared. The performance of Particle filter using Sampling Importance Resampling algorithm is found to be superior to that of Recursive Least Squared Error approach and Extended Kalman filter.

Subjects/Keywords: Localization; Particle Filter; RSSI; Extended Kalman Filter; Non-Liner Bayesian Estimation; Sequential Importance Sampling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Palvai, A. K. (2009). Passive RF Localization Based on RSSI Using Non-linear Bayesian Estimation . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/3407

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Palvai, Anoop Kumar. “Passive RF Localization Based on RSSI Using Non-linear Bayesian Estimation .” 2009. Thesis, George Mason University. Accessed September 20, 2019. http://hdl.handle.net/1920/3407.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Palvai, Anoop Kumar. “Passive RF Localization Based on RSSI Using Non-linear Bayesian Estimation .” 2009. Web. 20 Sep 2019.

Vancouver:

Palvai AK. Passive RF Localization Based on RSSI Using Non-linear Bayesian Estimation . [Internet] [Thesis]. George Mason University; 2009. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/1920/3407.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Palvai AK. Passive RF Localization Based on RSSI Using Non-linear Bayesian Estimation . [Thesis]. George Mason University; 2009. Available from: http://hdl.handle.net/1920/3407

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Waterloo

26. Won, Peter. Intelligent Fastening Tool Tracking Systems Using Hybrid Remote Sensing Technologies.

Degree: 2010, University of Waterloo

URL: http://hdl.handle.net/10012/5230

► This research focuses on the development of intelligent fastening tool tracking systems for the automotive industry to identify the fastened bolts. In order to accomplish… (more)

▼ This research focuses on the development of intelligent fastening tool tracking systems for the automotive industry to identify the fastened bolts. In order to accomplish such a task, the position of the tool tip must be identified because the tool tip position coincides with the head of the fastened bolt while the tool fastens the bolt. The proposed systems utilize an inertial measurement unit (IMU) and another sensor to track the position and orientation of the tool tip. To minimize the position and orientation calculation error, an IMU needs to be calibrated as accurately as possible. This research presents a novel triaxial accelerometer calibration technique that offers a high accuracy. The simulation and experimental results of the accelerometer calibration are presented. To identify the fastening action, an expert system is developed based on the sensor measurements. When a fastening action is identified, the system identifies the fastened bolt by using an expert system based on the position and orientation of the tool tip and the position and orientation of the bolt. Since each fastening procedure needs different accuracies and requirements, three different systems are proposed. The first system utilizes a triaxial magnetometer and an IMU to identify the fastened bolt. This system calculates the position and orientation by using an IMU. An expert system is used to identify the initial position, stationary state, and the fastened bolt. When the tool fastens a bolt, the proposed expert system detects the fastening action by triaxial accelerometer and triaxial magnetometer measurements. When the fastening action is detected, the system corrects the velocity and position error using zero velocity update (ZUPT). By using the corrected tool tip position and orientation, the system can identify the fastened bolts. Then, with the fastened bolt position, the position of the IMU is corrected. When the tool is stationary, the system corrects linear velocity error and reduces the position error. The experimental results demonstrate that the proposed system can identify fastened bolts if the angles of the bolts are different or the bolts are not closely placed. This low cost system does not require a line of sight, but has limited position accuracy. The second system utilizes an intelligent system that incorporates Kalman filters (KFs) and a fuzzy expert system to track the tip of a fastening tool and to identify the fastened bolt. This system employs one IMU and one encoder-based position sensor to determine the orientation and the centre of mass location of the tool. When the KF is used, the orientation error increases over time due to the integration step. Therefore, a fuzzy expert system is developed to correct the tilt angle error and orientation error. When the tool fastens a bolt, the system identifies the fastened bolt by applying the fuzzy expert system. When the fastened bolt is identified, the 3D orientation error of the tool is corrected by using the location and the orientation of the fastened…

Subjects/Keywords: Kalman filter; inertial measurement unit; position tracking; particle filter; fastening tool; orientation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Won, P. (2010). Intelligent Fastening Tool Tracking Systems Using Hybrid Remote Sensing Technologies. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/5230

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Won, Peter. “Intelligent Fastening Tool Tracking Systems Using Hybrid Remote Sensing Technologies.” 2010. Thesis, University of Waterloo. Accessed September 20, 2019. http://hdl.handle.net/10012/5230.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Won, Peter. “Intelligent Fastening Tool Tracking Systems Using Hybrid Remote Sensing Technologies.” 2010. Web. 20 Sep 2019.

Vancouver:

Won P. Intelligent Fastening Tool Tracking Systems Using Hybrid Remote Sensing Technologies. [Internet] [Thesis]. University of Waterloo; 2010. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/10012/5230.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Won P. Intelligent Fastening Tool Tracking Systems Using Hybrid Remote Sensing Technologies. [Thesis]. University of Waterloo; 2010. Available from: http://hdl.handle.net/10012/5230

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

27. Wang, Ren. Hybrid state estimation applications for joint traffic monitoring and incident detection.

Degree: PhD, Civil Engineering, 2015, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/88984

► This dissertation is motivated by the practical problems of highway traffic estimation and incident detection using measurements from various sensor types. It proposes a framework… (more)

▼ This dissertation is motivated by the practical problems of highway traffic estimation and incident detection using measurements from various sensor types. It proposes a framework to jointly estimate the traffic state and incidents in a hybrid state estimation problem where a continuous variable models the traffic state and a discrete model variable identifies the location and severity of an incident. Clearly, knowledge of an incident can improve post-incident traffic state estimates. Moreover, knowledge of the traffic state can be used to improve detection of incidents, by observing when the predicted traffic state differs significantly from the observed measurements. Two macroscopic traffic flow models are deployed to describe the evolution of traffic. Both the first order model and the second order model are extended to hybrid models by embedding a model parameter to denote the number of lanes open along the highway. The resulting traffic incident models are capable of describing traffic dynamics under both non-incident and incident scenarios that result in lane blockages. Next, several nonlinear filters are proposed to solve the joint traffic state estimation and incident detection problem. First, a multiple model particle filter and an interactive multiple model ensemble Kalman filter are proposed, where the particle filter or the ensemble Kalman filter are used to accommodate the nonlinearity of the traffic model, and multiple model methods are deployed to address the switching dynamics of traffic when incidents occur. Next, the multiple model particle filter is extended to a multiple model particle smoother to improve the estimation accuracy when data is limited. Finally, a variant of the multiple model particle filter, called the efficient multiple model particle filter, is developed for field implementations, which requires significantly less computation time compared to the other filters considered in this thesis. To validate the framework, the proposed nonlinear filters are implemented on the first order and second order traffic flow models, and tested in the microscopic traffic simulation software CORSIM and on field data collected on I-880 in California, which includes density measurements from inductive loops and speed measurements from GPS equipped vehicles. The results show that with either traffic flow model, the proposed traffic estimation algorithms are capable of jointly estimating the traffic state and detecting incidents when the traffic flow is high (i.e., when an incident results in congestion). The proposed algorithms are also compared with existing algorithms that independently estimate the traffic state or incidents. The results show that jointly estimating the state and incidents in one algorithm may perform better than two dedicated algorithms working independently, especially when loop detectors are sparse and the penetration rate of GPS equipped vehicles is high. Advisors/Committee Members: Work, Daniel (advisor), Work, Daniel (Committee Chair), Ouyang, Yanfeng (committee member), Peschel, Joshua (committee member), Sowers, Richard (committee member).

Subjects/Keywords: Traffic state estimation; Traffic incident detection; Particle filter; Kalman filter; Hybrid state estimation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, R. (2015). Hybrid state estimation applications for joint traffic monitoring and incident detection. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/88984

Chicago Manual of Style (16^th Edition):

Wang, Ren. “Hybrid state estimation applications for joint traffic monitoring and incident detection.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed September 20, 2019. http://hdl.handle.net/2142/88984.

MLA Handbook (7^th Edition):

Wang, Ren. “Hybrid state estimation applications for joint traffic monitoring and incident detection.” 2015. Web. 20 Sep 2019.

Vancouver:

Wang R. Hybrid state estimation applications for joint traffic monitoring and incident detection. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/2142/88984.

Council of Science Editors:

Wang R. Hybrid state estimation applications for joint traffic monitoring and incident detection. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/88984

University of Illinois – Urbana-Champaign

28. Baillio, Brad A. Multirobot Tethering for Localization and Control.

Degree: MS, Electrical and Computer Engineering, 2009, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/11975

► Particle filtering has proven to be an effective localization method for wheeled autonomous vehicles. For a given map, a sensor model, and observations, occasions arise… (more)

Subjects/Keywords: Particle filter; Tethering; Robotics; H infinity Control; Embedded; Linux; Bayes Filter; Laser Rangefinder (Lidar)

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APA (6^th Edition):

Baillio, B. A. (2009). Multirobot Tethering for Localization and Control. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/11975

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Baillio, Brad A. “Multirobot Tethering for Localization and Control.” 2009. Thesis, University of Illinois – Urbana-Champaign. Accessed September 20, 2019. http://hdl.handle.net/2142/11975.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Baillio, Brad A. “Multirobot Tethering for Localization and Control.” 2009. Web. 20 Sep 2019.

Vancouver:

Baillio BA. Multirobot Tethering for Localization and Control. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2009. [cited 2019 Sep 20]. Available from: http://hdl.handle.net/2142/11975.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Baillio BA. Multirobot Tethering for Localization and Control. [Thesis]. University of Illinois – Urbana-Champaign; 2009. Available from: http://hdl.handle.net/2142/11975

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brigham Young University

29. Huber, Braden Russell. Radio Determination on Mini-UAV Platforms: Tracking and Locating Radio Transmitters.

Degree: MS, 2009, Brigham Young University

URL: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2742&context=etd

► Aircraft in the US are equipped with Emergency Locator Transmitters (ELTs). In emergency situations these beacons are activated, providing a radio signal that can… (more)

Subjects/Keywords: radiolocation; radio direction finding; particle filter; unscented Kalman filter; state estimation; Computer Sciences

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APA (6^th Edition):

Huber, B. R. (2009). Radio Determination on Mini-UAV Platforms: Tracking and Locating Radio Transmitters. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2742&context=etd

Chicago Manual of Style (16^th Edition):

Huber, Braden Russell. “Radio Determination on Mini-UAV Platforms: Tracking and Locating Radio Transmitters.” 2009. Masters Thesis, Brigham Young University. Accessed September 20, 2019. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2742&context=etd.

MLA Handbook (7^th Edition):

Huber, Braden Russell. “Radio Determination on Mini-UAV Platforms: Tracking and Locating Radio Transmitters.” 2009. Web. 20 Sep 2019.

Vancouver:

Huber BR. Radio Determination on Mini-UAV Platforms: Tracking and Locating Radio Transmitters. [Internet] [Masters thesis]. Brigham Young University; 2009. [cited 2019 Sep 20]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2742&context=etd.

Council of Science Editors:

Huber BR. Radio Determination on Mini-UAV Platforms: Tracking and Locating Radio Transmitters. [Masters Thesis]. Brigham Young University; 2009. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2742&context=etd

Brigham Young University

30. Cannon, Brandon Jeffrey. Fault Detection for Unmanned Aerial Vehicles with Non-Redundant Sensors.

Degree: MS, 2014, Brigham Young University

URL: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6307&context=etd

► To operate, autonomous systems of necessity employ a variety of sensors to perceive their environment. Many small unmanned aerial vehicles (UAV) are unable to carry… (more)

▼ To operate, autonomous systems of necessity employ a variety of sensors to perceive their environment. Many small unmanned aerial vehicles (UAV) are unable to carry redundant sensors due to size, weight, and power (SWaP) constraints. Faults in these sensors can cause undesired behavior, including system instability. Thus, detection of faults in these non-redundant sensors is of paramount importance.The problem of detecting sensor faults in non-redundant sensors on board autonomous aircraft is non-trivial. Factors that make development of a solution difficult include both an inability to perfectly characterize systems and sensors as well as the SWaP constraints inherent with small UAV. An additional challenge is the ability of a fault-detection method to strike a balance between false-alarm rate and detection rate.This thesis explores two model-based methods of fault-detection for non-redundant sensors, a Kalman filter based method and a particle filter based method. The Kalman filter based method employs tests of mean and covariance on the normalized innovation sequence to detect faults, while the particle filter based method uses a function of the average particle weights.The Kalman filter based approach was implemented in real time on board an autonomous rotorcraft using an extended Kalman Filter (EKF). Faults tested included varied levels of bias, drift, and increased noise. Metrics included false-alarm rate, detection rate, and delay to detection. The particle filter based approach was implemented on a simulated system. This was then compared with an implementation of the EKF based approach for the same system. The same fault types and metrics were also used for these tests.The EKF based method of fault-detection performed well onboard the autonomous rotorcraft and should be generalizable to other systems for which an EKF or Kalman filter can be implemented. The theory indicates that the particle filter based algorithm should have performed better, though the simulations showed poor detection characteristics in comparison to the Kalman filter based method. Future work should be performed to explore improvements to the particle filter based method.

Subjects/Keywords: fault-detection; Kalman filter; particle filter; non-redundant sensor; estimator; UAV; unmanned aircraft; Mechanical Engineering

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APA (6^th Edition):

Cannon, B. J. (2014). Fault Detection for Unmanned Aerial Vehicles with Non-Redundant Sensors. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6307&context=etd

Chicago Manual of Style (16^th Edition):

Cannon, Brandon Jeffrey. “Fault Detection for Unmanned Aerial Vehicles with Non-Redundant Sensors.” 2014. Masters Thesis, Brigham Young University. Accessed September 20, 2019. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6307&context=etd.

MLA Handbook (7^th Edition):

Cannon, Brandon Jeffrey. “Fault Detection for Unmanned Aerial Vehicles with Non-Redundant Sensors.” 2014. Web. 20 Sep 2019.

Vancouver:

Cannon BJ. Fault Detection for Unmanned Aerial Vehicles with Non-Redundant Sensors. [Internet] [Masters thesis]. Brigham Young University; 2014. [cited 2019 Sep 20]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6307&context=etd.

Council of Science Editors:

Cannon BJ. Fault Detection for Unmanned Aerial Vehicles with Non-Redundant Sensors. [Masters Thesis]. Brigham Young University; 2014. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=6307&context=etd

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