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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Ye Z. A Discrete-time Particle Filter and Central Limit Theorem. [Internet] [Masters thesis]. University of Alberta; 2013. [cited 2021 Feb 27]. 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

2. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Barac D. Localization algorithms for indoor UAVs. [Internet] [Thesis]. Linköping UniversityLinköping University; 2011. [cited 2021 Feb 27]. 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

California State University – Chico

3. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Daley P. A REAL-TIME EFFICIENT PARTICLE FILTER ALGORITHM FOR REALISTIC TARGET TRACKING IN GAMES . [Internet] [Thesis]. California State University – Chico; 2012. [cited 2021 Feb 27]. 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

Delft University of Technology

4. Chitchian, M.M. (author). 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 (author). “Adapting Particle Filter Algorithms to the GPU Architecture.” 2011. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:9b11258e-cd9c-494d-bbb7-3b85c700b30f.

MLA Handbook (7^th Edition):

Chitchian, M M (author). “Adapting Particle Filter Algorithms to the GPU Architecture.” 2011. Web. 27 Feb 2021.

Vancouver:

Chitchian MM(. Adapting Particle Filter Algorithms to the GPU Architecture. [Internet] [Masters thesis]. Delft University of Technology; 2011. [cited 2021 Feb 27]. 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

NSYSU

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

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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Lin Y. Improved Particle Filter for Target Tracking in Decentralized Data Fusion System. [Internet] [Thesis]. NSYSU; 2009. [cited 2021 Feb 27]. 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

University of Alberta

6. 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 February 27, 2021. https://era.library.ualberta.ca/files/c6969z0831.

MLA Handbook (7^th Edition):

Wang, Xingpu. “Web Based Particle Filters.” 2015. Web. 27 Feb 2021.

Vancouver:

Wang X. Web Based Particle Filters. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2021 Feb 27]. 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

7. 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 February 27, 2021. 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. 27 Feb 2021.

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 2021 Feb 27]. 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

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

Degree: MS, Electrical Engineering, 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. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860

Chicago Manual of Style (16^th Edition):

Noor, Amina. “Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering.” 2012. Masters Thesis, Texas A&M University. Accessed February 27, 2021. http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860.

MLA Handbook (7^th Edition):

Noor, Amina. “Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering.” 2012. Web. 27 Feb 2021.

Vancouver:

Noor A. Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering. [Internet] [Masters thesis]. Texas A&M University; 2012. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860.

Council of Science Editors:

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

Penn State University

9. Mattes, Richard J. Vehicle Localization With A Downward Facing LIDAR.

Degree: 2015, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/26471

► Autonomous, or self-driving vehicles hold much promise for the future of personal transportation. Autonomous vehicles have the potential to remove the human error from driving,… (more)

Subjects/Keywords: localization; particle filter; autonomous vehicles; simulation; LIDAR

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APA (6^th Edition):

Mattes, R. J. (2015). Vehicle Localization With A Downward Facing LIDAR. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/26471

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):

Mattes, Richard J. “Vehicle Localization With A Downward Facing LIDAR.” 2015. Thesis, Penn State University. Accessed February 27, 2021. https://submit-etda.libraries.psu.edu/catalog/26471.

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

MLA Handbook (7^th Edition):

Mattes, Richard J. “Vehicle Localization With A Downward Facing LIDAR.” 2015. Web. 27 Feb 2021.

Vancouver:

Mattes RJ. Vehicle Localization With A Downward Facing LIDAR. [Internet] [Thesis]. Penn State University; 2015. [cited 2021 Feb 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/26471.

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

Council of Science Editors:

Mattes RJ. Vehicle Localization With A Downward Facing LIDAR. [Thesis]. Penn State University; 2015. Available from: https://submit-etda.libraries.psu.edu/catalog/26471

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

Delft University of Technology

10. Giannopoulos, Makis (author). Hierarchical Mapping: A Rao-Blackwellized Particle Filter based hierarchical SLAM framework.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:2d3532a6-fda2-44ea-b743-b643ad7d122c

► Mobile robots need to be fully autonomous in order to perform their tasks inside their environment. To do that, robots need to have an understanding… (more)

▼ Mobile robots need to be fully autonomous in order to perform their tasks inside their environment. To do that, robots need to have an understanding of their environment, so that they can successfully localize and navigate themselves within it. The understanding of the robots' environment is created by solving the SLAM (Simultaneous Localization And Mapping) problem. The SLAM problem is usually approached using probabilistic laws; both the robot's path and the environment are estimated and represented by probability distributions. The estimate of the environment is incorporated into a map, which could either be one global map or a network of smaller connected local maps. In small areas where the sensors' errors and inaccuracies remain low, the creation of one global map is preferred. However, as the area grows larger, the global map becomes inaccurate due to accumulated errors. Therefore the strategy of creating a network of local maps is employed. Even though this strategy produces low error results, there is still a limit beyond which it does not scale. Specifically, as the mapping area grows even wider, the number of local maps increases which in turn causes the amount of required memory to increase. In addition, when it comes to loop closure or in other words the procedure of estimating whether the robot returned to a previously visited point in the map, the required computational power increases linearly or sometimes quadratically with respect to the number of local maps. Consequently, SLAM algorithms suffer from scalability problems, especially when the mapping area becomes too large. Since robots are real-world devices, mapping solutions should be efficient and applicable to the robot's resources, even when these resources are limited. In this thesis we propose a framework which addresses this scalability issue, aiming to reduce the computational needs and memory usage during the SLAM procedure. Additionally, our framework intends to facilitate loop closure, which can become troublesome in large environments. Our framework uses multiple sensors and creates a network of hierarchically structured local maps. In order to assess our framework, we compare it to a technique which produces a network of non-hierarchically structured local maps. Our experiments show that in large areas our framework works ideally; the speedup is encouraging and the RAM memory overhead is negligible or conditionally lower. Advisors/Committee Members: van Genderen, Arjan (mentor), Cotofana, Sorin (graduation committee), Hakkennes, Edwin (graduation committee), Verhoeven, Chris (graduation committee), Chronopoulos, Dimitrios (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: SLAM; mapping; hierarchical mapping; Particle Filter

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

APA (6^th Edition):

Giannopoulos, M. (. (2017). Hierarchical Mapping: A Rao-Blackwellized Particle Filter based hierarchical SLAM framework. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:2d3532a6-fda2-44ea-b743-b643ad7d122c

Chicago Manual of Style (16^th Edition):

Giannopoulos, Makis (author). “Hierarchical Mapping: A Rao-Blackwellized Particle Filter based hierarchical SLAM framework.” 2017. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:2d3532a6-fda2-44ea-b743-b643ad7d122c.

MLA Handbook (7^th Edition):

Giannopoulos, Makis (author). “Hierarchical Mapping: A Rao-Blackwellized Particle Filter based hierarchical SLAM framework.” 2017. Web. 27 Feb 2021.

Vancouver:

Giannopoulos M(. Hierarchical Mapping: A Rao-Blackwellized Particle Filter based hierarchical SLAM framework. [Internet] [Masters thesis]. Delft University of Technology; 2017. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:2d3532a6-fda2-44ea-b743-b643ad7d122c.

Council of Science Editors:

Giannopoulos M(. Hierarchical Mapping: A Rao-Blackwellized Particle Filter based hierarchical SLAM framework. [Masters Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:2d3532a6-fda2-44ea-b743-b643ad7d122c

Delft University of Technology

11. van Groeningen, Tom (author). Human presence detection using a particle filter on ultrasonic micro-Doppler measurements for assisting rescue work in large buildings.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:467ea25a-e5f3-47e6-857b-3e0510ff52de

►

When the re brigade arrives at a burning building, it is of vital importance that people who are still inside can quickly be found. In… (more)

Subjects/Keywords: micro doppler; particle filter; Gaussian approximation

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APA (6^th Edition):

van Groeningen, T. (. (2018). Human presence detection using a particle filter on ultrasonic micro-Doppler measurements for assisting rescue work in large buildings. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:467ea25a-e5f3-47e6-857b-3e0510ff52de

Chicago Manual of Style (16^th Edition):

van Groeningen, Tom (author). “Human presence detection using a particle filter on ultrasonic micro-Doppler measurements for assisting rescue work in large buildings.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:467ea25a-e5f3-47e6-857b-3e0510ff52de.

MLA Handbook (7^th Edition):

van Groeningen, Tom (author). “Human presence detection using a particle filter on ultrasonic micro-Doppler measurements for assisting rescue work in large buildings.” 2018. Web. 27 Feb 2021.

Vancouver:

van Groeningen T(. Human presence detection using a particle filter on ultrasonic micro-Doppler measurements for assisting rescue work in large buildings. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:467ea25a-e5f3-47e6-857b-3e0510ff52de.

Council of Science Editors:

van Groeningen T(. Human presence detection using a particle filter on ultrasonic micro-Doppler measurements for assisting rescue work in large buildings. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:467ea25a-e5f3-47e6-857b-3e0510ff52de

Delft University of Technology

12. Beers, Karlijn (author). Data assimilation, Geomechanical parameter estimation in the Groningen hydrocarbon reservoir from PS-InSAR measurements with a particle filter.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:6db5ad75-8b34-4fce-a570-070c7b6bf6b3

► This thesis explores the usage of the particle filter as a data assimilation technique to estimate subsurface processes, such as reservoir volume change from space-geodetic… (more)

Subjects/Keywords: Data assimilation; Particle Filter; InSAR; Groningen

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APA (6^th Edition):

Beers, K. (. (2018). Data assimilation, Geomechanical parameter estimation in the Groningen hydrocarbon reservoir from PS-InSAR measurements with a particle filter. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6db5ad75-8b34-4fce-a570-070c7b6bf6b3

Chicago Manual of Style (16^th Edition):

Beers, Karlijn (author). “Data assimilation, Geomechanical parameter estimation in the Groningen hydrocarbon reservoir from PS-InSAR measurements with a particle filter.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:6db5ad75-8b34-4fce-a570-070c7b6bf6b3.

MLA Handbook (7^th Edition):

Beers, Karlijn (author). “Data assimilation, Geomechanical parameter estimation in the Groningen hydrocarbon reservoir from PS-InSAR measurements with a particle filter.” 2018. Web. 27 Feb 2021.

Vancouver:

Beers K(. Data assimilation, Geomechanical parameter estimation in the Groningen hydrocarbon reservoir from PS-InSAR measurements with a particle filter. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:6db5ad75-8b34-4fce-a570-070c7b6bf6b3.

Council of Science Editors:

Beers K(. Data assimilation, Geomechanical parameter estimation in the Groningen hydrocarbon reservoir from PS-InSAR measurements with a particle filter. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:6db5ad75-8b34-4fce-a570-070c7b6bf6b3

University of Illinois – Urbana-Champaign

13. Aranguiz, Dario. Low-cost time-of-flight-based localization techniques for robotic applications.

Degree: MS, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/99528

► This thesis provides an overview of the localization techniques used by the robotic platform Mousr, as well as the modeling assumptions and simulator scaffolding used… (more)

Subjects/Keywords: Robotics; Localization; Particle filter; Time of flight

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APA (6^th Edition):

Aranguiz, D. (2017). Low-cost time-of-flight-based localization techniques for robotic applications. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99528

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):

Aranguiz, Dario. “Low-cost time-of-flight-based localization techniques for robotic applications.” 2017. Thesis, University of Illinois – Urbana-Champaign. Accessed February 27, 2021. http://hdl.handle.net/2142/99528.

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

MLA Handbook (7^th Edition):

Aranguiz, Dario. “Low-cost time-of-flight-based localization techniques for robotic applications.” 2017. Web. 27 Feb 2021.

Vancouver:

Aranguiz D. Low-cost time-of-flight-based localization techniques for robotic applications. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2142/99528.

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

Council of Science Editors:

Aranguiz D. Low-cost time-of-flight-based localization techniques for robotic applications. [Thesis]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99528

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

Georgia Tech

14. 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 February 27, 2021. http://hdl.handle.net/1853/56265.

MLA Handbook (7^th Edition):

Leonard, Andrew. “Vehicle tracking using ultra-wideband radar.” 2016. Web. 27 Feb 2021.

Vancouver:

Leonard A. Vehicle tracking using ultra-wideband radar. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2021 Feb 27]. 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

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

Degree: PhD, Computer Science, 2011, University of Canterbury

URL: http://dx.doi.org/10.26021/1741

► 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. (Doctoral Dissertation). University of Canterbury. Retrieved from http://dx.doi.org/10.26021/1741

Chicago Manual of Style (16^th Edition):

Gao, Hongzhi. “Real Time Human Tracking in Unconstrained Environments.” 2011. Doctoral Dissertation, University of Canterbury. Accessed February 27, 2021. http://dx.doi.org/10.26021/1741.

MLA Handbook (7^th Edition):

Gao, Hongzhi. “Real Time Human Tracking in Unconstrained Environments.” 2011. Web. 27 Feb 2021.

Vancouver:

Gao H. Real Time Human Tracking in Unconstrained Environments. [Internet] [Doctoral dissertation]. University of Canterbury; 2011. [cited 2021 Feb 27]. Available from: http://dx.doi.org/10.26021/1741.

Council of Science Editors:

Gao H. Real Time Human Tracking in Unconstrained Environments. [Doctoral Dissertation]. University of Canterbury; 2011. Available from: http://dx.doi.org/10.26021/1741

Vanderbilt University

16. Patki, Akash. Particle Filter based SLAM to map random environments using “iRobot Roomba”.

Degree: MS, Computer Science, 2011, Vanderbilt University

URL: http://hdl.handle.net/1803/15177

► For any mobile robot application it is important that a robot knows its location in an operating environment. The map for the operating environment may… (more)

Subjects/Keywords: Particle Filter; Monte Carlo; Mapping; SLAM; Localization

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APA (6^th Edition):

Patki, A. (2011). Particle Filter based SLAM to map random environments using “iRobot Roomba”. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/15177

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):

Patki, Akash. “Particle Filter based SLAM to map random environments using “iRobot Roomba”.” 2011. Thesis, Vanderbilt University. Accessed February 27, 2021. http://hdl.handle.net/1803/15177.

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

MLA Handbook (7^th Edition):

Patki, Akash. “Particle Filter based SLAM to map random environments using “iRobot Roomba”.” 2011. Web. 27 Feb 2021.

Vancouver:

Patki A. Particle Filter based SLAM to map random environments using “iRobot Roomba”. [Internet] [Thesis]. Vanderbilt University; 2011. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1803/15177.

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

Council of Science Editors:

Patki A. Particle Filter based SLAM to map random environments using “iRobot Roomba”. [Thesis]. Vanderbilt University; 2011. Available from: http://hdl.handle.net/1803/15177

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

University of Texas – Austin

17. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Shen, Xiaohu a2c. Bayesian inference methods for next generation DNA sequencing. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2021 Feb 27]. 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

University of New South Wales

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

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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Chen Y. Facial Segmentation Using Boosted Dynamic Mixture Active Shape Model. [Internet] [Masters thesis]. University of New South Wales; 2015. [cited 2021 Feb 27]. 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

19. 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 (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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Särkkä S. Recursive Bayesian Inference on Stochastic Differential Equations. [Internet] [Thesis]. Helsinki University of Technology; 2006. [cited 2021 Feb 27]. 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

20. 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 (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 February 27, 2021. https://era.library.ualberta.ca/files/n009w369x.

MLA Handbook (7^th Edition):

Shenoy, Arjun Vsiwanath. “Unconstrained nonlinear state estimation for chemical processes.” 2010. Web. 27 Feb 2021.

Vancouver:

Shenoy AV. Unconstrained nonlinear state estimation for chemical processes. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2021 Feb 27]. 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

Georgia Tech

21. Nakamura, Takuma. Multiple-hypothesis vision-based landing autonomy.

Degree: PhD, Aerospace Engineering, 2018, Georgia Tech

URL: http://hdl.handle.net/1853/62195

► Unmanned aerial vehicles (UAVs) need humans in the mission loop for many tasks, and landing is one of the tasks that typically involves a human… (more)

▼ Unmanned aerial vehicles (UAVs) need humans in the mission loop for many tasks, and landing is one of the tasks that typically involves a human pilot. This is because of the complexity of a maneuver itself and flight-critical factors such as recognition of a landing zone, collision avoidance, assessment of landing sites, and decision to abort the maneuver. Another critical aspect to be considered is the reliance of UAVs on GPS (global positioning system). A GPS system is not a reliable solution for landing in some scenarios (e.g. delivering a package in an urban city, and a surveillance UAV repatriating a home ship with the jammed signals), and a landing solely based on a GPS extremely decreases the UAV operation envelope. Vision is promising to achieve fully autonomous landing because it is a rich-sensing, light, affordable device that functions without any external resource. Although vision is a powerful tool for autonomous landing, the use of vision for state estimation requires extensive consideration. Firstly, vision-based landing faces a problem of occlusion. The target detected at a high altitude would be lost at certain altitudes while a vehicle descends; however, a small visual target can not be recognized at high altitude. Second, standard filtering methods such as extended Kalman filter (EKF) face difficulty due to the complex dynamics of the measurement error created due to the discrete pixel space, conversion from the pixel to physical units, the complex camera model, and complexity of detection algorithms. The vision sensor produces an unfixed number of measurements with each image, and the measurements may include false positives. Plus, the estimation system is excessively tasked in realistic conditions. The landing site would be moving, tilted, or close to an obstacle. The available landing location may not be limited to one. In addition to assessing these statuses, understanding the confidence of the estimations is also the tasks of the vision, and the decisions to initiate, continue, and abort the mission are made based on the estimated states and confidence. The system that handles those issues and consistently produces the navigation solution while a vehicle lands eliminates one of the limitations of the autonomous UAV operation. This thesis presents a novel state estimation system for UAV landing. In this system, vision data is used to both estimate the state of the vehicle and map the state of the landing target (position, velocity, and attitude) within the framework of simultaneous localization and mapping (SLAM). Using the SLAM framework, the system becomes resilient to a loss of GPS and other sensor failures. A novel vision algorithm that detects a portion of the marker is developed, and the stochastic properties of the algorithm are studied. This algorithm extends the detectable range of the vision system for any known marker. However, this vision algorithm produces highly nonlinear, non-Gaussian, and multi-modal error distribution, and a naive implementation of filters would not accurately… Advisors/Committee Members: Johnson, Eric (advisor), Tsiotras, Panagiotis (advisor), Feron, Eric (committee member), Vela, Patricio (committee member), Hays, James (committee member).

Subjects/Keywords: Sensor fusion; Kalman filter; Particle filter; SLAM; Computer vision

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APA (6^th Edition):

Nakamura, T. (2018). Multiple-hypothesis vision-based landing autonomy. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62195

Chicago Manual of Style (16^th Edition):

Nakamura, Takuma. “Multiple-hypothesis vision-based landing autonomy.” 2018. Doctoral Dissertation, Georgia Tech. Accessed February 27, 2021. http://hdl.handle.net/1853/62195.

MLA Handbook (7^th Edition):

Nakamura, Takuma. “Multiple-hypothesis vision-based landing autonomy.” 2018. Web. 27 Feb 2021.

Vancouver:

Nakamura T. Multiple-hypothesis vision-based landing autonomy. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1853/62195.

Council of Science Editors:

Nakamura T. Multiple-hypothesis vision-based landing autonomy. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/62195

Penn State University

22. Chen, Meng. Dual Estimation in State Space Models with Violation to Normality: A Comparison between the Extended Kalman Filter and the Particle Filter.

Degree: 2019, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/17297mxc681

► In the field of psychology and developmental science, researchers often study the change of some underlying latent construct over time. It is of interest both… (more)

Subjects/Keywords: state space model; Kalman filter; particle filter; dual estimation

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APA (6^th Edition):

Chen, M. (2019). Dual Estimation in State Space Models with Violation to Normality: A Comparison between the Extended Kalman Filter and the Particle Filter. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/17297mxc681

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):

Chen, Meng. “Dual Estimation in State Space Models with Violation to Normality: A Comparison between the Extended Kalman Filter and the Particle Filter.” 2019. Thesis, Penn State University. Accessed February 27, 2021. https://submit-etda.libraries.psu.edu/catalog/17297mxc681.

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

MLA Handbook (7^th Edition):

Chen, Meng. “Dual Estimation in State Space Models with Violation to Normality: A Comparison between the Extended Kalman Filter and the Particle Filter.” 2019. Web. 27 Feb 2021.

Vancouver:

Chen M. Dual Estimation in State Space Models with Violation to Normality: A Comparison between the Extended Kalman Filter and the Particle Filter. [Internet] [Thesis]. Penn State University; 2019. [cited 2021 Feb 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/17297mxc681.

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

Council of Science Editors:

Chen M. Dual Estimation in State Space Models with Violation to Normality: A Comparison between the Extended Kalman Filter and the Particle Filter. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/17297mxc681

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

McMaster University

23. Leach, James. ADVANCING SEQUENTIAL DATA ASSIMILATION METHODS FOR ENHANCED HYDROLOGIC FORECASTING IN SEMI-URBAN WATERSHEDS.

Degree: PhD, 2019, McMaster University

URL: http://hdl.handle.net/11375/25083

►

Accurate hydrologic forecasting is vital for proper water resource management. Practices that are impacted by these forecasts include power generation, reservoir management, agricultural water use,… (more)

▼

Accurate hydrologic forecasting is vital for proper water resource management. Practices that are impacted by these forecasts include power generation, reservoir management, agricultural water use, and flood early warning systems. Despite these needs, the models largely used are simplifications of the real world and are therefore imperfect. The forecasters face other challenges in addition to the model uncertainty, which includes imperfect observations used for model calibration and validation, imperfect meteorological forecasts, and the ability to effectively communicate forecast results to decision-makers. Bayesian methods are commonly used to address some of these issues, and this thesis will be focused on improving methods related to recursive Bayesian estimation, more commonly known as data assimilation. Data assimilation is a means to optimally account for the uncertainties in observations, models, and forcing data. In the literature, data assimilation for urban hydrologic and flood forecasting is rare; therefore the main areas of study in this thesis are urban and semi-urban watersheds. By providing improvements to data assimilation methods, both hydrologic and flood forecasting can be enhanced in these areas. This work explored the use of alternative data products as a type of observation that can be assimilated to improve hydrologic forecasting in an urban watershed. The impact of impervious surfaces in urban and semi-urban watersheds was also evaluated in regards to its impact on remotely sensed soil moisture assimilation. Lack of observations is another issue when it comes to data assimilation, particularly in semi- or fully-distributed models; because of this, an improved method for updating locations which do not have observations was developed which utilizes information theory’s mutual information. Finally, we explored extending data assimilation into the short-term forecast by using prior knowledge of how a model will respond to forecasted forcing data. Results from this work found that using alternative data products such as those from the Snow Data Assimilation System or the Soil Moisture and Ocean Salinity mission, can be effective at improving hydrologic forecasting in urban watersheds. They also were effective at identifying a limiting imperviousness threshold for soil moisture assimilation into urban and semi-urban watersheds. Additionally, the inclusion of mutual information between gauged and ungauged locations in a semi-distributed hydrologic model was able to provide better state updates in models. Finally, by extending data assimilation into the short-term forecast, the reliability of the forecasts could be improved substantially.

Dissertation

Doctor of Philosophy (PhD)

The ability to accurately model hydrological systems is essential, as that allows for better planning and decision making in water resources management. The better we can forecast the hydrologic response to rain and snowmelt events, the better we can plan and manage our water resources. This includes better…

Advisors/Committee Members: Coulibaly, Paulin, Civil Engineering.

Subjects/Keywords: Data Assimilation; Ensemble Forecasting; Ensemble Kalman FIlter; Particle Filter; Streamflow

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APA (6^th Edition):

Leach, J. (2019). ADVANCING SEQUENTIAL DATA ASSIMILATION METHODS FOR ENHANCED HYDROLOGIC FORECASTING IN SEMI-URBAN WATERSHEDS. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/25083

Chicago Manual of Style (16^th Edition):

Leach, James. “ADVANCING SEQUENTIAL DATA ASSIMILATION METHODS FOR ENHANCED HYDROLOGIC FORECASTING IN SEMI-URBAN WATERSHEDS.” 2019. Doctoral Dissertation, McMaster University. Accessed February 27, 2021. http://hdl.handle.net/11375/25083.

MLA Handbook (7^th Edition):

Leach, James. “ADVANCING SEQUENTIAL DATA ASSIMILATION METHODS FOR ENHANCED HYDROLOGIC FORECASTING IN SEMI-URBAN WATERSHEDS.” 2019. Web. 27 Feb 2021.

Vancouver:

Leach J. ADVANCING SEQUENTIAL DATA ASSIMILATION METHODS FOR ENHANCED HYDROLOGIC FORECASTING IN SEMI-URBAN WATERSHEDS. [Internet] [Doctoral dissertation]. McMaster University; 2019. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/11375/25083.

Council of Science Editors:

Leach J. ADVANCING SEQUENTIAL DATA ASSIMILATION METHODS FOR ENHANCED HYDROLOGIC FORECASTING IN SEMI-URBAN WATERSHEDS. [Doctoral Dissertation]. McMaster University; 2019. Available from: http://hdl.handle.net/11375/25083

Virginia Commonwealth University

24. 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://doi.org/10.25772/1BF9-H342 ; 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://doi.org/10.25772/1BF9-H342 ; 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 February 27, 2021. https://doi.org/10.25772/1BF9-H342 ; 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. 27 Feb 2021.

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 2021 Feb 27]. Available from: https://doi.org/10.25772/1BF9-H342 ; 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://doi.org/10.25772/1BF9-H342 ; 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

25. 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 February 27, 2021. 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. 27 Feb 2021.

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 2021 Feb 27]. 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 Tennessee – Knoxville

26. 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 (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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Collins JP. Comparison of Methods for Estimating Stochastic Volatility. [Internet] [Thesis]. University of Tennessee – Knoxville; 2013. [cited 2021 Feb 27]. 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

27. 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 (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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Tapiero Bernal JE. Bayesian Estimation For Tracking Of Spiraling Reentry Vehicles. [Internet] [Thesis]. Marquette University; 2013. [cited 2021 Feb 27]. 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

University of California – Berkeley

28. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Lei J. Non-linear Filtering for State Space Models - High-Dimensional Applications and Theoretical Results. [Internet] [Thesis]. University of California – Berkeley; 2010. [cited 2021 Feb 27]. 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

Brigham Young University

29. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Cannon BJ. Fault Detection for Unmanned Aerial Vehicles with Non-Redundant Sensors. [Internet] [Masters thesis]. Brigham Young University; 2014. [cited 2021 Feb 27]. 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

University of Illinois – Urbana-Champaign

30. 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 February 27, 2021. 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. 27 Feb 2021.

Vancouver:

Wang R. Hybrid state estimation applications for joint traffic monitoring and incident detection. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Feb 27]. 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

Open Access Theses and Dissertations