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You searched for subject:(Multitarget tracking Nonlinear filtering Probability Hypothesis Density filter Splines). Showing records 1 – 30 of 36859 total matches.

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McMaster University

1. Sithiravel, Rajiv. B-Spline Based Multitarget Tracking.

Degree: PhD, 2014, McMaster University

Multitarget tracking in the presence of false alarm is a difficult problem to consider. The objective of multitarget tracking is to estimate the number of… (more)

Subjects/Keywords: Multitarget tracking, Nonlinear filtering, Probability Hypothesis Density filter, Splines; Maneuvering target tracking, Maximum-Likelihood-Probabilistic Data Association Algorithm, Track initialization, Low observable target tracking, Joint Maximum-Likelihood-Probabilistic Data Association Algorithm

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APA (6th Edition):

Sithiravel, R. (2014). B-Spline Based Multitarget Tracking. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/16068

Chicago Manual of Style (16th Edition):

Sithiravel, Rajiv. “B-Spline Based Multitarget Tracking.” 2014. Doctoral Dissertation, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/16068.

MLA Handbook (7th Edition):

Sithiravel, Rajiv. “B-Spline Based Multitarget Tracking.” 2014. Web. 16 Apr 2021.

Vancouver:

Sithiravel R. B-Spline Based Multitarget Tracking. [Internet] [Doctoral dissertation]. McMaster University; 2014. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/16068.

Council of Science Editors:

Sithiravel R. B-Spline Based Multitarget Tracking. [Doctoral Dissertation]. McMaster University; 2014. Available from: http://hdl.handle.net/11375/16068


McMaster University

2. Punithakumar, K. Nonlinear Filtering Algorithms for Multitarget Tracking.

Degree: 2007, McMaster University

Tracking multiple targets with uncertain target dynamics is a difficult problem, especially with nonlinear state and/or measurement equations. Random finite set theory provides a rigorous… (more)

Subjects/Keywords: Probability Hypothesis Density filter; multitarget tracking; SNR targets; sensors

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APA (6th Edition):

Punithakumar, K. (2007). Nonlinear Filtering Algorithms for Multitarget Tracking. (Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/16632

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

Chicago Manual of Style (16th Edition):

Punithakumar, K. “Nonlinear Filtering Algorithms for Multitarget Tracking.” 2007. Thesis, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/16632.

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

MLA Handbook (7th Edition):

Punithakumar, K. “Nonlinear Filtering Algorithms for Multitarget Tracking.” 2007. Web. 16 Apr 2021.

Vancouver:

Punithakumar K. Nonlinear Filtering Algorithms for Multitarget Tracking. [Internet] [Thesis]. McMaster University; 2007. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/16632.

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

Council of Science Editors:

Punithakumar K. Nonlinear Filtering Algorithms for Multitarget Tracking. [Thesis]. McMaster University; 2007. Available from: http://hdl.handle.net/11375/16632

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


McMaster University

3. Dunne, Darcy. Random Finite Set Methods for Multitarget Tracking.

Degree: DEng, 2013, McMaster University

Multiple target tracking (MTT) is a major area that occurs in a variety of real world systems. The problem involves the detection and estimation… (more)

Subjects/Keywords: Multiple target tracking; Random Finite Sets; Probability Hypothesis Density; Multitarget Multi-Bernoulli; Intensity filter; Multi-Vehicle Systems and Air Traffic Control; Multi-Vehicle Systems and Air Traffic Control

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APA (6th Edition):

Dunne, D. (2013). Random Finite Set Methods for Multitarget Tracking. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/12941

Chicago Manual of Style (16th Edition):

Dunne, Darcy. “Random Finite Set Methods for Multitarget Tracking.” 2013. Doctoral Dissertation, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/12941.

MLA Handbook (7th Edition):

Dunne, Darcy. “Random Finite Set Methods for Multitarget Tracking.” 2013. Web. 16 Apr 2021.

Vancouver:

Dunne D. Random Finite Set Methods for Multitarget Tracking. [Internet] [Doctoral dissertation]. McMaster University; 2013. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/12941.

Council of Science Editors:

Dunne D. Random Finite Set Methods for Multitarget Tracking. [Doctoral Dissertation]. McMaster University; 2013. Available from: http://hdl.handle.net/11375/12941

4. Ghadami, Soheil. The Evaluation of the Gaussian Mixture Probability Hypothesis Density Filter Applied in a Stereo Vision System.

Degree: 2011, , School of Engineering

  In this thesis, the performance of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter using a pair of stereo vision system to overcome label… (more)

Subjects/Keywords: Gaussian Mixture probability Hypothesis Density Filter; Human Motion Tracking; Data Association; Occlusion Handling; Stereo Vision

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APA (6th Edition):

Ghadami, S. (2011). The Evaluation of the Gaussian Mixture Probability Hypothesis Density Filter Applied in a Stereo Vision System. (Thesis). , School of Engineering. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5461

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

Chicago Manual of Style (16th Edition):

Ghadami, Soheil. “The Evaluation of the Gaussian Mixture Probability Hypothesis Density Filter Applied in a Stereo Vision System.” 2011. Thesis, , School of Engineering. Accessed April 16, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5461.

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

MLA Handbook (7th Edition):

Ghadami, Soheil. “The Evaluation of the Gaussian Mixture Probability Hypothesis Density Filter Applied in a Stereo Vision System.” 2011. Web. 16 Apr 2021.

Vancouver:

Ghadami S. The Evaluation of the Gaussian Mixture Probability Hypothesis Density Filter Applied in a Stereo Vision System. [Internet] [Thesis]. , School of Engineering; 2011. [cited 2021 Apr 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5461.

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

Council of Science Editors:

Ghadami S. The Evaluation of the Gaussian Mixture Probability Hypothesis Density Filter Applied in a Stereo Vision System. [Thesis]. , School of Engineering; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5461

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

5. WANG YADONG. Stochastic methods for bayesian filtering and their applications to multicamera multitarget tracking.

Degree: 2008, National University of Singapore

Subjects/Keywords: Bayesian filtering; target tracking; particle filter; sequential Monte Carlo method; random set; probability hypothesis density

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APA (6th Edition):

YADONG, W. (2008). Stochastic methods for bayesian filtering and their applications to multicamera multitarget tracking. (Thesis). National University of Singapore. Retrieved from https://scholarbank.nus.edu.sg/handle/10635/28161

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

Chicago Manual of Style (16th Edition):

YADONG, WANG. “Stochastic methods for bayesian filtering and their applications to multicamera multitarget tracking.” 2008. Thesis, National University of Singapore. Accessed April 16, 2021. https://scholarbank.nus.edu.sg/handle/10635/28161.

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

MLA Handbook (7th Edition):

YADONG, WANG. “Stochastic methods for bayesian filtering and their applications to multicamera multitarget tracking.” 2008. Web. 16 Apr 2021.

Vancouver:

YADONG W. Stochastic methods for bayesian filtering and their applications to multicamera multitarget tracking. [Internet] [Thesis]. National University of Singapore; 2008. [cited 2021 Apr 16]. Available from: https://scholarbank.nus.edu.sg/handle/10635/28161.

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

Council of Science Editors:

YADONG W. Stochastic methods for bayesian filtering and their applications to multicamera multitarget tracking. [Thesis]. National University of Singapore; 2008. Available from: https://scholarbank.nus.edu.sg/handle/10635/28161

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


University of New South Wales

6. Narula, Karan. Compressed estimation in coupled high-dimensional processes.

Degree: Mechanical & Manufacturing Engineering, 2019, University of New South Wales

 The state estimation of high-dimensional problems is a topic of great interest in diverse research and application areas. In such problems, the conventional methods, although… (more)

Subjects/Keywords: Stochastic PDEs; Generalised compressed kalman filter; High-dimensional estimation; Estimation; Kalman filters; Probability density function; Filtering algorithms; Stochastic processes; Filtering theory; Nonlinear estimation

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APA (6th Edition):

Narula, K. (2019). Compressed estimation in coupled high-dimensional processes. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/63269 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:60059/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Narula, Karan. “Compressed estimation in coupled high-dimensional processes.” 2019. Doctoral Dissertation, University of New South Wales. Accessed April 16, 2021. http://handle.unsw.edu.au/1959.4/63269 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:60059/SOURCE02?view=true.

MLA Handbook (7th Edition):

Narula, Karan. “Compressed estimation in coupled high-dimensional processes.” 2019. Web. 16 Apr 2021.

Vancouver:

Narula K. Compressed estimation in coupled high-dimensional processes. [Internet] [Doctoral dissertation]. University of New South Wales; 2019. [cited 2021 Apr 16]. Available from: http://handle.unsw.edu.au/1959.4/63269 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:60059/SOURCE02?view=true.

Council of Science Editors:

Narula K. Compressed estimation in coupled high-dimensional processes. [Doctoral Dissertation]. University of New South Wales; 2019. Available from: http://handle.unsw.edu.au/1959.4/63269 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:60059/SOURCE02?view=true

7. OYEKANLU, Emmanuel- Adebomi. Human Motion Tracking The Gaussian Mixture Probability Hypothesis Density Filter Approach.

Degree: 2010, , School of Engineering

  Motion tracking is an important part of the Intelligent Vision Agent System, IVAS. In this thesis, the Gaussian mixture approximation of the Probability Hypothesis(more)

Subjects/Keywords: Probability Hypothesis Density filter; GM-PHD; IVAS; Performance Evaluation; Tracking

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APA (6th Edition):

OYEKANLU, E. A. (2010). Human Motion Tracking The Gaussian Mixture Probability Hypothesis Density Filter Approach. (Thesis). , School of Engineering. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3700

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

Chicago Manual of Style (16th Edition):

OYEKANLU, Emmanuel- Adebomi. “Human Motion Tracking The Gaussian Mixture Probability Hypothesis Density Filter Approach.” 2010. Thesis, , School of Engineering. Accessed April 16, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3700.

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

MLA Handbook (7th Edition):

OYEKANLU, Emmanuel- Adebomi. “Human Motion Tracking The Gaussian Mixture Probability Hypothesis Density Filter Approach.” 2010. Web. 16 Apr 2021.

Vancouver:

OYEKANLU EA. Human Motion Tracking The Gaussian Mixture Probability Hypothesis Density Filter Approach. [Internet] [Thesis]. , School of Engineering; 2010. [cited 2021 Apr 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3700.

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

Council of Science Editors:

OYEKANLU EA. Human Motion Tracking The Gaussian Mixture Probability Hypothesis Density Filter Approach. [Thesis]. , School of Engineering; 2010. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3700

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


University of Minnesota

8. Schlenker, Lauren. Spacecraft Relative Navigation Using Random Finite Sets.

Degree: MS, Aerospace Engineering and Mechanics, 2019, University of Minnesota

 Future space missions require that spacecraft have onboard capability to autonomously navigate non-cooperative environments for rendezvous and proximity operations (RPO). Current relative navigation filters can… (more)

Subjects/Keywords: Particle filter; Probability hypothesis density filter; Random finite sets; Relative navigation; SLAM; Spacecraft navigation

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APA (6th Edition):

Schlenker, L. (2019). Spacecraft Relative Navigation Using Random Finite Sets. (Masters Thesis). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206138

Chicago Manual of Style (16th Edition):

Schlenker, Lauren. “Spacecraft Relative Navigation Using Random Finite Sets.” 2019. Masters Thesis, University of Minnesota. Accessed April 16, 2021. http://hdl.handle.net/11299/206138.

MLA Handbook (7th Edition):

Schlenker, Lauren. “Spacecraft Relative Navigation Using Random Finite Sets.” 2019. Web. 16 Apr 2021.

Vancouver:

Schlenker L. Spacecraft Relative Navigation Using Random Finite Sets. [Internet] [Masters thesis]. University of Minnesota; 2019. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11299/206138.

Council of Science Editors:

Schlenker L. Spacecraft Relative Navigation Using Random Finite Sets. [Masters Thesis]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206138


University of New South Wales

9. Pasha, Syed. Problems in nonlinear Bayesian filtering.

Degree: Electrical Engineering & Telecommunications, 2009, University of New South Wales

 This dissertation presents solutions to two open problems in estimation theory. The first is a tractable analytical solution for problems in multi-target filtering which are… (more)

Subjects/Keywords: Probability Hypothesis Density (PHD); Thesis Digitisation Program; Nonlinear filtering; Markov multi-target model; Multi-target filtering

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APA (6th Edition):

Pasha, S. (2009). Problems in nonlinear Bayesian filtering. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/43792 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:7253/SOURCE1?view=true

Chicago Manual of Style (16th Edition):

Pasha, Syed. “Problems in nonlinear Bayesian filtering.” 2009. Doctoral Dissertation, University of New South Wales. Accessed April 16, 2021. http://handle.unsw.edu.au/1959.4/43792 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:7253/SOURCE1?view=true.

MLA Handbook (7th Edition):

Pasha, Syed. “Problems in nonlinear Bayesian filtering.” 2009. Web. 16 Apr 2021.

Vancouver:

Pasha S. Problems in nonlinear Bayesian filtering. [Internet] [Doctoral dissertation]. University of New South Wales; 2009. [cited 2021 Apr 16]. Available from: http://handle.unsw.edu.au/1959.4/43792 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:7253/SOURCE1?view=true.

Council of Science Editors:

Pasha S. Problems in nonlinear Bayesian filtering. [Doctoral Dissertation]. University of New South Wales; 2009. Available from: http://handle.unsw.edu.au/1959.4/43792 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:7253/SOURCE1?view=true


Linköping University

10. Jerrelind, Jakob. Tracking of Pedestrians Using Multi-Target Tracking Methods with a Group Representation.

Degree: Automatic Control, 2020, Linköping University

  Multi-target tracking (MTT) methods estimate the trajectory of targets from noisy measurement; therefore, they can be used to handle the pedestrian-vehicle interaction for a… (more)

Subjects/Keywords: Multiple target tracking; random finite sets; probability hypothesis density; PHD; cardinalized; CPHD; Control Engineering; Reglerteknik

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APA (6th Edition):

Jerrelind, J. (2020). Tracking of Pedestrians Using Multi-Target Tracking Methods with a Group Representation. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172579

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

Chicago Manual of Style (16th Edition):

Jerrelind, Jakob. “Tracking of Pedestrians Using Multi-Target Tracking Methods with a Group Representation.” 2020. Thesis, Linköping University. Accessed April 16, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172579.

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

MLA Handbook (7th Edition):

Jerrelind, Jakob. “Tracking of Pedestrians Using Multi-Target Tracking Methods with a Group Representation.” 2020. Web. 16 Apr 2021.

Vancouver:

Jerrelind J. Tracking of Pedestrians Using Multi-Target Tracking Methods with a Group Representation. [Internet] [Thesis]. Linköping University; 2020. [cited 2021 Apr 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172579.

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

Council of Science Editors:

Jerrelind J. Tracking of Pedestrians Using Multi-Target Tracking Methods with a Group Representation. [Thesis]. Linköping University; 2020. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-172579

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


University of New Orleans

11. Wu, Jiande. Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications.

Degree: PhD, Electrical Engineering, 2014, University of New Orleans

  Particle filtering has been a very popular method to solve nonlinear/non-Gaussian state estimation problems for more than twenty years. Particle filters (PFs) have found… (more)

Subjects/Keywords: nonlinear filtering, particle filter, particle flow filter, parallel and distributed computing, GPU, computer cluster, FPGA, target tracking; Electrical and Computer Engineering

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APA (6th Edition):

Wu, J. (2014). Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications. (Doctoral Dissertation). University of New Orleans. Retrieved from https://scholarworks.uno.edu/td/1953

Chicago Manual of Style (16th Edition):

Wu, Jiande. “Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications.” 2014. Doctoral Dissertation, University of New Orleans. Accessed April 16, 2021. https://scholarworks.uno.edu/td/1953.

MLA Handbook (7th Edition):

Wu, Jiande. “Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications.” 2014. Web. 16 Apr 2021.

Vancouver:

Wu J. Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications. [Internet] [Doctoral dissertation]. University of New Orleans; 2014. [cited 2021 Apr 16]. Available from: https://scholarworks.uno.edu/td/1953.

Council of Science Editors:

Wu J. Parallel Computing of Particle Filtering Algorithms for Target Tracking Applications. [Doctoral Dissertation]. University of New Orleans; 2014. Available from: https://scholarworks.uno.edu/td/1953


Washington State University

12. [No author]. Unsupervised Multi-resident Tracking in Smart Environments .

Degree: 2019, Washington State University

 Aging is a global challenge that our society will face in the next few decades. Smart environment and ambient assisted living (AAL) offer promising technologies… (more)

Subjects/Keywords: Electrical engineering; Computer science; Ambient Assisted Living; Indoor Mobility; Multi-Resident Tracking; Predictability; Probability Hypothesis Density Tracking; Smart Home

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APA (6th Edition):

author], [. (2019). Unsupervised Multi-resident Tracking in Smart Environments . (Thesis). Washington State University. Retrieved from http://hdl.handle.net/2376/17889

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

Chicago Manual of Style (16th Edition):

author], [No. “Unsupervised Multi-resident Tracking in Smart Environments .” 2019. Thesis, Washington State University. Accessed April 16, 2021. http://hdl.handle.net/2376/17889.

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

MLA Handbook (7th Edition):

author], [No. “Unsupervised Multi-resident Tracking in Smart Environments .” 2019. Web. 16 Apr 2021.

Vancouver:

author] [. Unsupervised Multi-resident Tracking in Smart Environments . [Internet] [Thesis]. Washington State University; 2019. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2376/17889.

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

Council of Science Editors:

author] [. Unsupervised Multi-resident Tracking in Smart Environments . [Thesis]. Washington State University; 2019. Available from: http://hdl.handle.net/2376/17889

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

13. Jiang, Jiewei. Multiple Nueral Artifacts Suppression Using Gaussian Mixture Modeling and Probability Hypothesis Density Filtering.

Degree: Electrical Engineering, 2014, Arizona State University

Subjects/Keywords: Electrical engineering; Artifacts Suppression; Feature extraction; Multiple target tracking; Neural activity; Probability hypothesis density filter

…3.3 Probability Hypothesis Density Filtering for Multiple Object Tracking In many real… …3.3.2 Probability Hypothesis Density Filter Implementation using Particle Filtering In our… …filtering, the particle probability hypothesis density filter (PPHDF) has the three main… …problem, the probability hypothesis density filter (PHDF) is a filter that can… …Probability Hypothesis Density Filtering Formulation The PHDF is based on a recursion propagating… 

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APA (6th Edition):

Jiang, J. (2014). Multiple Nueral Artifacts Suppression Using Gaussian Mixture Modeling and Probability Hypothesis Density Filtering. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/25909

Chicago Manual of Style (16th Edition):

Jiang, Jiewei. “Multiple Nueral Artifacts Suppression Using Gaussian Mixture Modeling and Probability Hypothesis Density Filtering.” 2014. Masters Thesis, Arizona State University. Accessed April 16, 2021. http://repository.asu.edu/items/25909.

MLA Handbook (7th Edition):

Jiang, Jiewei. “Multiple Nueral Artifacts Suppression Using Gaussian Mixture Modeling and Probability Hypothesis Density Filtering.” 2014. Web. 16 Apr 2021.

Vancouver:

Jiang J. Multiple Nueral Artifacts Suppression Using Gaussian Mixture Modeling and Probability Hypothesis Density Filtering. [Internet] [Masters thesis]. Arizona State University; 2014. [cited 2021 Apr 16]. Available from: http://repository.asu.edu/items/25909.

Council of Science Editors:

Jiang J. Multiple Nueral Artifacts Suppression Using Gaussian Mixture Modeling and Probability Hypothesis Density Filtering. [Masters Thesis]. Arizona State University; 2014. Available from: http://repository.asu.edu/items/25909


University of Florida

14. Yoon, Jae Myung. A Comparative Study of Adaptive MCMC Based Particle Filtering Methods.

Degree: MS, Mechanical Engineering - Mechanical and Aerospace Engineering, 2012, University of Florida

 In this thesis, we present a comparative study of conventional particle filtering (PF) algorithms for tracking applications. Through the review from the generic PF to… (more)

Subjects/Keywords: Approximation; Covariance; Graphics; Kalman filters; Noise measurement; Particle density; Quantum statistics; Standard deviation; State estimation; Trajectories; filter  – filtering  – mcmc  – particle  – tracking

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

APA (6th Edition):

Yoon, J. M. (2012). A Comparative Study of Adaptive MCMC Based Particle Filtering Methods. (Masters Thesis). University of Florida. Retrieved from https://ufdc.ufl.edu/UFE0044219

Chicago Manual of Style (16th Edition):

Yoon, Jae Myung. “A Comparative Study of Adaptive MCMC Based Particle Filtering Methods.” 2012. Masters Thesis, University of Florida. Accessed April 16, 2021. https://ufdc.ufl.edu/UFE0044219.

MLA Handbook (7th Edition):

Yoon, Jae Myung. “A Comparative Study of Adaptive MCMC Based Particle Filtering Methods.” 2012. Web. 16 Apr 2021.

Vancouver:

Yoon JM. A Comparative Study of Adaptive MCMC Based Particle Filtering Methods. [Internet] [Masters thesis]. University of Florida; 2012. [cited 2021 Apr 16]. Available from: https://ufdc.ufl.edu/UFE0044219.

Council of Science Editors:

Yoon JM. A Comparative Study of Adaptive MCMC Based Particle Filtering Methods. [Masters Thesis]. University of Florida; 2012. Available from: https://ufdc.ufl.edu/UFE0044219

15. Petetin, Yohan. Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles markoviens : Single and multiple object(s) Bayesian restoration algorithms for Markovian models.

Degree: Docteur es, Mathématiques appliquées, 2013, Evry, Institut national des télécommunications

Cette thèse est consacrée au problème d'estimation bayésienne pour le filtrage statistique, dont l'objectif est d'estimer récursivement des états inconnus à partir d'un historique d'observations,… (more)

Subjects/Keywords: Filtrage particulaire; Méthodes de Monte Carlo; Rao-Blackwell; Probability Hypothesis Density; Filtrage multi-objets; Modèles de Markov cachés; Modèles de Markov couple et triplet; Particle filtering; Monte Carlo methods; Rao-Blackwell; Probability Hypothesis Density; Multi-object filtering; Hidden Markov models; Pairwise and triplet Markov models

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

APA (6th Edition):

Petetin, Y. (2013). Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles markoviens : Single and multiple object(s) Bayesian restoration algorithms for Markovian models. (Doctoral Dissertation). Evry, Institut national des télécommunications. Retrieved from http://www.theses.fr/2013TELE0032

Chicago Manual of Style (16th Edition):

Petetin, Yohan. “Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles markoviens : Single and multiple object(s) Bayesian restoration algorithms for Markovian models.” 2013. Doctoral Dissertation, Evry, Institut national des télécommunications. Accessed April 16, 2021. http://www.theses.fr/2013TELE0032.

MLA Handbook (7th Edition):

Petetin, Yohan. “Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles markoviens : Single and multiple object(s) Bayesian restoration algorithms for Markovian models.” 2013. Web. 16 Apr 2021.

Vancouver:

Petetin Y. Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles markoviens : Single and multiple object(s) Bayesian restoration algorithms for Markovian models. [Internet] [Doctoral dissertation]. Evry, Institut national des télécommunications; 2013. [cited 2021 Apr 16]. Available from: http://www.theses.fr/2013TELE0032.

Council of Science Editors:

Petetin Y. Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles markoviens : Single and multiple object(s) Bayesian restoration algorithms for Markovian models. [Doctoral Dissertation]. Evry, Institut national des télécommunications; 2013. Available from: http://www.theses.fr/2013TELE0032


University of Tennessee – Knoxville

16. Li, Zhiqiang. Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems.

Degree: 2012, University of Tennessee – Knoxville

 Various particle filters have been proposed and their convergence to the optimal filter are obtained for finite time intervals. However, uniform convergence results have been… (more)

Subjects/Keywords: nonlinear filtering; branching particle aproximation; Probability

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APA (6th Edition):

Li, Z. (2012). Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/1322

Chicago Manual of Style (16th Edition):

Li, Zhiqiang. “Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems.” 2012. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed April 16, 2021. https://trace.tennessee.edu/utk_graddiss/1322.

MLA Handbook (7th Edition):

Li, Zhiqiang. “Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems.” 2012. Web. 16 Apr 2021.

Vancouver:

Li Z. Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2012. [cited 2021 Apr 16]. Available from: https://trace.tennessee.edu/utk_graddiss/1322.

Council of Science Editors:

Li Z. Stability of Nonlinear Filters and Branching Particle Approximations to The Filtering Problems. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2012. Available from: https://trace.tennessee.edu/utk_graddiss/1322


McMaster University

17. Nadarajah, N. Retrodiction for Multitarget Tracking.

Degree: PhD, 2009, McMaster University

  Multi-Target Tracking (MTT), where the number of targets as well as their states are time-varying, concerns with the estimation of both the number of… (more)

Subjects/Keywords: electrical and computer engineering; retrodiction; multitarget tracking; smoothing; filtering

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APA (6th Edition):

Nadarajah, N. (2009). Retrodiction for Multitarget Tracking. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/17364

Chicago Manual of Style (16th Edition):

Nadarajah, N. “Retrodiction for Multitarget Tracking.” 2009. Doctoral Dissertation, McMaster University. Accessed April 16, 2021. http://hdl.handle.net/11375/17364.

MLA Handbook (7th Edition):

Nadarajah, N. “Retrodiction for Multitarget Tracking.” 2009. Web. 16 Apr 2021.

Vancouver:

Nadarajah N. Retrodiction for Multitarget Tracking. [Internet] [Doctoral dissertation]. McMaster University; 2009. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/11375/17364.

Council of Science Editors:

Nadarajah N. Retrodiction for Multitarget Tracking. [Doctoral Dissertation]. McMaster University; 2009. Available from: http://hdl.handle.net/11375/17364


Université de Bordeaux I

18. Pace, Michele. Stochastic models and methods for multi-object tracking : Méthodes et modèles stochastiques pour le suivi multi-objets.

Degree: Docteur es, Mathématiques appliquées, 2011, Université de Bordeaux I

La poursuite multi-cibles a pour objet le suivi d’un ensemble de cibles mobiles à partir de données obtenues séquentiellement. Ce problème est particulièrement complexe du… (more)

Subjects/Keywords: Processus de branchements; Filtres particulaires; Filtrage non-linéaire multi-cibles; Systèmes de particules de type champ moyen; Semi-groupes de Feynman-Kac; Filtre PHD; Propriétés de concentration exponentielle; Inégalités de contraction fonctionnelles; Measure-valued equations; Non-linear multi-target filtering; Bernoulli filter; Probability Hypothesis Density filter; Interacting particle systems; Particle filters; Sequential Monte Carlo methods; Exponential concentration inequalities; Semigroup stability; Functional contraction inequalities

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

APA (6th Edition):

Pace, M. (2011). Stochastic models and methods for multi-object tracking : Méthodes et modèles stochastiques pour le suivi multi-objets. (Doctoral Dissertation). Université de Bordeaux I. Retrieved from http://www.theses.fr/2011BOR14291

Chicago Manual of Style (16th Edition):

Pace, Michele. “Stochastic models and methods for multi-object tracking : Méthodes et modèles stochastiques pour le suivi multi-objets.” 2011. Doctoral Dissertation, Université de Bordeaux I. Accessed April 16, 2021. http://www.theses.fr/2011BOR14291.

MLA Handbook (7th Edition):

Pace, Michele. “Stochastic models and methods for multi-object tracking : Méthodes et modèles stochastiques pour le suivi multi-objets.” 2011. Web. 16 Apr 2021.

Vancouver:

Pace M. Stochastic models and methods for multi-object tracking : Méthodes et modèles stochastiques pour le suivi multi-objets. [Internet] [Doctoral dissertation]. Université de Bordeaux I; 2011. [cited 2021 Apr 16]. Available from: http://www.theses.fr/2011BOR14291.

Council of Science Editors:

Pace M. Stochastic models and methods for multi-object tracking : Méthodes et modèles stochastiques pour le suivi multi-objets. [Doctoral Dissertation]. Université de Bordeaux I; 2011. Available from: http://www.theses.fr/2011BOR14291


University of Illinois – Urbana-Champaign

19. 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

 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 (6th 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 (16th 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 April 16, 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 (7th Edition):

Medarametla, Krishna Kalyan. “Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter.” 2014. Web. 16 Apr 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 Apr 16]. 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


Loughborough University

20. Daniyan, Abdullahi. Advanced signal processing techniques for multi-target tracking.

Degree: PhD, 2018, Loughborough University

 The multi-target tracking problem essentially involves the recursive joint estimation of the state of unknown and time-varying number of targets present in a tracking scene,… (more)

Subjects/Keywords: 621.382; Target tracking; Bayesian inference; Bayesian estimation; Signal Processing; Kalman filter; Random finite sets; RFS; Particle filter; PHD filter; Probability hypothesis filter; CPHD filter; Sequential Monte Carlo; Game theory; Regret matching; Correlated equilibrium; Passive radar; Statistical signal processing; Probability; GLMB; Labelled random finite sets; Kalman gain; Poisson mixture; B-spline

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APA (6th Edition):

Daniyan, A. (2018). Advanced signal processing techniques for multi-target tracking. (Doctoral Dissertation). Loughborough University. Retrieved from http://hdl.handle.net/2134/35277

Chicago Manual of Style (16th Edition):

Daniyan, Abdullahi. “Advanced signal processing techniques for multi-target tracking.” 2018. Doctoral Dissertation, Loughborough University. Accessed April 16, 2021. http://hdl.handle.net/2134/35277.

MLA Handbook (7th Edition):

Daniyan, Abdullahi. “Advanced signal processing techniques for multi-target tracking.” 2018. Web. 16 Apr 2021.

Vancouver:

Daniyan A. Advanced signal processing techniques for multi-target tracking. [Internet] [Doctoral dissertation]. Loughborough University; 2018. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2134/35277.

Council of Science Editors:

Daniyan A. Advanced signal processing techniques for multi-target tracking. [Doctoral Dissertation]. Loughborough University; 2018. Available from: http://hdl.handle.net/2134/35277


The Ohio State University

21. Koroglu, Muhammed Taha. Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching.

Degree: PhD, Electrical and Computer Engineering, 2020, The Ohio State University

 Inertial sensors became wearable with the advances in sensing and computing technologies in the last two decades. Captured motion data can be used to build… (more)

Subjects/Keywords: Engineering; Electrical Engineering; pedestrian tracking; MEMS; inertial navigation system; INS; inertial measurement unit; IMU; building information model; BIM; map-matching; multiple hypothesis testing; Kalman Filter; Particle Filter; Bayesian Filtering; pedestrian INS, zero velocity; ZUPT

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APA (6th Edition):

Koroglu, M. T. (2020). Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298

Chicago Manual of Style (16th Edition):

Koroglu, Muhammed Taha. “Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching.” 2020. Doctoral Dissertation, The Ohio State University. Accessed April 16, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298.

MLA Handbook (7th Edition):

Koroglu, Muhammed Taha. “Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching.” 2020. Web. 16 Apr 2021.

Vancouver:

Koroglu MT. Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching. [Internet] [Doctoral dissertation]. The Ohio State University; 2020. [cited 2021 Apr 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298.

Council of Science Editors:

Koroglu MT. Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching. [Doctoral Dissertation]. The Ohio State University; 2020. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298

22. Malik, Zohaib Mansoor. Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system.

Degree: Mathematics and Natural Sciences, 2012, University of Gävle

  A general automotive navigation system is a satellite navigation system designed for use inautomobiles. It typically uses GPS to acquire position data to locate… (more)

Subjects/Keywords: Kalman filter; Inertial Navigation; Temporal filtering; Accelerometer; Location tracking

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APA (6th Edition):

Malik, Z. M. (2012). Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system. (Thesis). University of Gävle. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13261

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

Chicago Manual of Style (16th Edition):

Malik, Zohaib Mansoor. “Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system.” 2012. Thesis, University of Gävle. Accessed April 16, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13261.

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

MLA Handbook (7th Edition):

Malik, Zohaib Mansoor. “Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system.” 2012. Web. 16 Apr 2021.

Vancouver:

Malik ZM. Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system. [Internet] [Thesis]. University of Gävle; 2012. [cited 2021 Apr 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13261.

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

Council of Science Editors:

Malik ZM. Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system. [Thesis]. University of Gävle; 2012. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13261

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

23. Malik, Zohaib Mansoor. Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system.

Degree: Mathematics and Natural Sciences, 2012, University of Gävle

  A general automotive navigation system is a satellite navigation system designed for use in automobiles. It typically uses GPS to acquire position data to… (more)

Subjects/Keywords: Kalman filter; Inertial sensor; Location tracking; Temporal filtering

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APA (6th Edition):

Malik, Z. M. (2012). Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system. (Thesis). University of Gävle. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13225

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

Chicago Manual of Style (16th Edition):

Malik, Zohaib Mansoor. “Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system.” 2012. Thesis, University of Gävle. Accessed April 16, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13225.

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

MLA Handbook (7th Edition):

Malik, Zohaib Mansoor. “Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system.” 2012. Web. 16 Apr 2021.

Vancouver:

Malik ZM. Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system. [Internet] [Thesis]. University of Gävle; 2012. [cited 2021 Apr 16]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13225.

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

Council of Science Editors:

Malik ZM. Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system. [Thesis]. University of Gävle; 2012. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13225

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


University of Michigan

24. Kreucher, Christopher M. An information-based approach to sensor resource allocation.

Degree: PhD, Electrical engineering, 2005, University of Michigan

 This work addresses the problem of scheduling the resources of agile sensors. We advocate an information-based approach, where sensor tasking decisions are made based on… (more)

Subjects/Keywords: Approach; Based; Information Theory; Multitarget Tracking; Particle Filtering; Resource Allocation; Sensor

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APA (6th Edition):

Kreucher, C. M. (2005). An information-based approach to sensor resource allocation. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/124852

Chicago Manual of Style (16th Edition):

Kreucher, Christopher M. “An information-based approach to sensor resource allocation.” 2005. Doctoral Dissertation, University of Michigan. Accessed April 16, 2021. http://hdl.handle.net/2027.42/124852.

MLA Handbook (7th Edition):

Kreucher, Christopher M. “An information-based approach to sensor resource allocation.” 2005. Web. 16 Apr 2021.

Vancouver:

Kreucher CM. An information-based approach to sensor resource allocation. [Internet] [Doctoral dissertation]. University of Michigan; 2005. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2027.42/124852.

Council of Science Editors:

Kreucher CM. An information-based approach to sensor resource allocation. [Doctoral Dissertation]. University of Michigan; 2005. Available from: http://hdl.handle.net/2027.42/124852


Univerzitet u Beogradu

25. Tadić, Predrag R. Статистичка обрада сигнала за дијагностику грешака и предиктивно одржавање система.

Degree: Elektrotehnički fakultet, 2016, Univerzitet u Beogradu

Електротехника и рачунарство - Управљање системима и обрада сигнала / Electrical and computer engineering - System control and signal processing

Циљ истраживања представљеног у овој… (more)

Subjects/Keywords: Fault detection and isolation. Nonlinear systems. Particle filter. Sequential hypothesis testin

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APA (6th Edition):

Tadić, P. R. (2016). Статистичка обрада сигнала за дијагностику грешака и предиктивно одржавање система. (Thesis). Univerzitet u Beogradu. Retrieved from https://fedorabg.bg.ac.rs/fedora/get/o:11324/bdef:Content/get

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

Chicago Manual of Style (16th Edition):

Tadić, Predrag R. “Статистичка обрада сигнала за дијагностику грешака и предиктивно одржавање система.” 2016. Thesis, Univerzitet u Beogradu. Accessed April 16, 2021. https://fedorabg.bg.ac.rs/fedora/get/o:11324/bdef:Content/get.

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

MLA Handbook (7th Edition):

Tadić, Predrag R. “Статистичка обрада сигнала за дијагностику грешака и предиктивно одржавање система.” 2016. Web. 16 Apr 2021.

Vancouver:

Tadić PR. Статистичка обрада сигнала за дијагностику грешака и предиктивно одржавање система. [Internet] [Thesis]. Univerzitet u Beogradu; 2016. [cited 2021 Apr 16]. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:11324/bdef:Content/get.

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

Council of Science Editors:

Tadić PR. Статистичка обрада сигнала за дијагностику грешака и предиктивно одржавање система. [Thesis]. Univerzitet u Beogradu; 2016. Available from: https://fedorabg.bg.ac.rs/fedora/get/o:11324/bdef:Content/get

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


University of Illinois – Urbana-Champaign

26. Yang, Tao. Feedback particle filter and its applications.

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

 The purpose of nonlinear filtering is to extract useful information from noisy sensor data. It finds applications in all disciplines of science and engineering, including… (more)

Subjects/Keywords: Nonlinear filtering; estimation; particle filtering; statistical signal processing; optimal transportation; target tracking

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

APA (6th Edition):

Yang, T. (2014). Feedback particle filter and its applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/50708

Chicago Manual of Style (16th Edition):

Yang, Tao. “Feedback particle filter and its applications.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 16, 2021. http://hdl.handle.net/2142/50708.

MLA Handbook (7th Edition):

Yang, Tao. “Feedback particle filter and its applications.” 2014. Web. 16 Apr 2021.

Vancouver:

Yang T. Feedback particle filter and its applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/2142/50708.

Council of Science Editors:

Yang T. Feedback particle filter and its applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/50708


Texas A&M University

27. Weisman, Ryan Michael 1984-. Nonlinear Transformations and Filtering Theory for Space Operations.

Degree: PhD, Aerospace Engineering, 2012, Texas A&M University

 Decisions for asset allocation and protection are predicated upon accurate knowledge of the current operating environment as well as correctly characterizing the evolution of the… (more)

Subjects/Keywords: filter initialization; uncertainty estimation; bayesian filtering; nonlinear estimation; space surveillance

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APA (6th Edition):

Weisman, R. M. 1. (2012). Nonlinear Transformations and Filtering Theory for Space Operations. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/148211

Chicago Manual of Style (16th Edition):

Weisman, Ryan Michael 1984-. “Nonlinear Transformations and Filtering Theory for Space Operations.” 2012. Doctoral Dissertation, Texas A&M University. Accessed April 16, 2021. http://hdl.handle.net/1969.1/148211.

MLA Handbook (7th Edition):

Weisman, Ryan Michael 1984-. “Nonlinear Transformations and Filtering Theory for Space Operations.” 2012. Web. 16 Apr 2021.

Vancouver:

Weisman RM1. Nonlinear Transformations and Filtering Theory for Space Operations. [Internet] [Doctoral dissertation]. Texas A&M University; 2012. [cited 2021 Apr 16]. Available from: http://hdl.handle.net/1969.1/148211.

Council of Science Editors:

Weisman RM1. Nonlinear Transformations and Filtering Theory for Space Operations. [Doctoral Dissertation]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/148211


Penn State University

28. Hussain, Magni Stentoft. Adaptive Digital Filtering Using the Bio-Inspired Firefly Algorithm.

Degree: 2019, Penn State University

 In recent literature, bio-inspired optimization algorithms have been shown to perform well in IIR and nonlinear applications. A relatively new bio-inspired optimization algorithm, the Firefly… (more)

Subjects/Keywords: Adaptive Filtering; Firefly Algorithm; IIR Filter; Nonlinear Filter; System Identification; Fetal Electrocardiogram; Adaptive Noise Cancelling

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APA (6th Edition):

Hussain, M. S. (2019). Adaptive Digital Filtering Using the Bio-Inspired Firefly Algorithm. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/17117msh5334

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

Chicago Manual of Style (16th Edition):

Hussain, Magni Stentoft. “Adaptive Digital Filtering Using the Bio-Inspired Firefly Algorithm.” 2019. Thesis, Penn State University. Accessed April 16, 2021. https://submit-etda.libraries.psu.edu/catalog/17117msh5334.

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

MLA Handbook (7th Edition):

Hussain, Magni Stentoft. “Adaptive Digital Filtering Using the Bio-Inspired Firefly Algorithm.” 2019. Web. 16 Apr 2021.

Vancouver:

Hussain MS. Adaptive Digital Filtering Using the Bio-Inspired Firefly Algorithm. [Internet] [Thesis]. Penn State University; 2019. [cited 2021 Apr 16]. Available from: https://submit-etda.libraries.psu.edu/catalog/17117msh5334.

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

Council of Science Editors:

Hussain MS. Adaptive Digital Filtering Using the Bio-Inspired Firefly Algorithm. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/17117msh5334

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


Queensland University of Technology

29. Techakesari, Onvaree. Filter and control performance bounds in the presence of model uncertainties with aerospace applications.

Degree: 2013, Queensland University of Technology

 This thesis establishes performance properties for approximate filters and controllers that are designed on the basis of approximate dynamic system representations. These performance properties provide… (more)

Subjects/Keywords: Approximating Filter; Nonlinear Filtering; Filter Stability; Model Approximation; Relative Entropy; Hybrid Filter; Control Stability; Air Traffic Separation Management; Practical Stability

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

APA (6th Edition):

Techakesari, O. (2013). Filter and control performance bounds in the presence of model uncertainties with aerospace applications. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/63956/

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

Chicago Manual of Style (16th Edition):

Techakesari, Onvaree. “Filter and control performance bounds in the presence of model uncertainties with aerospace applications.” 2013. Thesis, Queensland University of Technology. Accessed April 16, 2021. https://eprints.qut.edu.au/63956/.

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

MLA Handbook (7th Edition):

Techakesari, Onvaree. “Filter and control performance bounds in the presence of model uncertainties with aerospace applications.” 2013. Web. 16 Apr 2021.

Vancouver:

Techakesari O. Filter and control performance bounds in the presence of model uncertainties with aerospace applications. [Internet] [Thesis]. Queensland University of Technology; 2013. [cited 2021 Apr 16]. Available from: https://eprints.qut.edu.au/63956/.

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

Council of Science Editors:

Techakesari O. Filter and control performance bounds in the presence of model uncertainties with aerospace applications. [Thesis]. Queensland University of Technology; 2013. Available from: https://eprints.qut.edu.au/63956/

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


University of Edinburgh

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

Degree: PhD, 2010, University of Edinburgh

 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)

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 (6th 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 (16th Edition):

Zhong, Xionghu. “Bayesian framework for multiple acoustic source tracking.” 2010. Doctoral Dissertation, University of Edinburgh. Accessed April 16, 2021. http://hdl.handle.net/1842/4752.

MLA Handbook (7th Edition):

Zhong, Xionghu. “Bayesian framework for multiple acoustic source tracking.” 2010. Web. 16 Apr 2021.

Vancouver:

Zhong X. Bayesian framework for multiple acoustic source tracking. [Internet] [Doctoral dissertation]. University of Edinburgh; 2010. [cited 2021 Apr 16]. 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

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