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Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks

The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the d...

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Autores principales: Hu, Yumei, Pan, Quan, Deng, Bao, Guo, Zhen, Li, Menghua, Chen, Lifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453888/
https://www.ncbi.nlm.nih.gov/pubmed/37628265
http://dx.doi.org/10.3390/e25081235
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author Hu, Yumei
Pan, Quan
Deng, Bao
Guo, Zhen
Li, Menghua
Chen, Lifeng
author_facet Hu, Yumei
Pan, Quan
Deng, Bao
Guo, Zhen
Li, Menghua
Chen, Lifeng
author_sort Hu, Yumei
collection PubMed
description The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the degree of nonlinearity of the underlying scenario. In this paper, two methods for computing the variational density, namely, the natural gradient method and the simultaneous perturbation stochastic method, are used to implement a variational Bayesian Kalman filter for maneuvering target tracking using Doppler measurements. The latter are collected from a set of sensors subject to single-hop network constraints. We propose a distributed fusion variational Bayesian Kalman filter for a networked maneuvering target tracking scenario and both of the evidence lower bound and the posterior Cramér–Rao lower bound of the proposed methods are presented. The simulation results are compared with centralized fusion in terms of posterior Cramér–Rao lower bounds, root-mean-squared errors and the 3 [Formula: see text] bound.
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spelling pubmed-104538882023-08-26 Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks Hu, Yumei Pan, Quan Deng, Bao Guo, Zhen Li, Menghua Chen, Lifeng Entropy (Basel) Article The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the variational density in the iteration and the degree of nonlinearity of the underlying scenario. In this paper, two methods for computing the variational density, namely, the natural gradient method and the simultaneous perturbation stochastic method, are used to implement a variational Bayesian Kalman filter for maneuvering target tracking using Doppler measurements. The latter are collected from a set of sensors subject to single-hop network constraints. We propose a distributed fusion variational Bayesian Kalman filter for a networked maneuvering target tracking scenario and both of the evidence lower bound and the posterior Cramér–Rao lower bound of the proposed methods are presented. The simulation results are compared with centralized fusion in terms of posterior Cramér–Rao lower bounds, root-mean-squared errors and the 3 [Formula: see text] bound. MDPI 2023-08-18 /pmc/articles/PMC10453888/ /pubmed/37628265 http://dx.doi.org/10.3390/e25081235 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hu, Yumei
Pan, Quan
Deng, Bao
Guo, Zhen
Li, Menghua
Chen, Lifeng
Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks
title Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks
title_full Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks
title_fullStr Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks
title_full_unstemmed Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks
title_short Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks
title_sort variational bayesian algorithms for maneuvering target tracking with nonlinear measurements in sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453888/
https://www.ncbi.nlm.nih.gov/pubmed/37628265
http://dx.doi.org/10.3390/e25081235
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