An Improved Particle Filter for Target Tracking in Sensor Systems

An Improved Particle Filter for Target Tracking in Sensor Systems

Sensor systems are not always equipped with the ability to track targets. Sudden maneuvers of a target can have a great impact on the sensor system, which will increase the miss rate and rate of false target detection. The use of the generic particle filter (PF) algorithm is well known for target tr...

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Main Authors: Wang, Xue, Wang, Sheng, Ma, Jun-Jie
Format: Online
Language:English
Published: Molecular Diversity Preservation International (MDPI) 2007
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3756717/
id pubmed-3756717
recordtype oai_dc
spelling pubmed-37567172013-08-29 An Improved Particle Filter for Target Tracking in Sensor Systems Wang, Xue Wang, Sheng Ma, Jun-Jie Full Paper Sensor systems are not always equipped with the ability to track targets. Sudden maneuvers of a target can have a great impact on the sensor system, which will increase the miss rate and rate of false target detection. The use of the generic particle filter (PF) algorithm is well known for target tracking, but it can not overcome the degeneracy of particles and cumulation of estimation errors. In this paper, we propose an improved PF algorithm called PF-RBF. This algorithm uses the radial-basis function network (RBFN) in the sampling step for dynamically constructing the process model from observations and updating the value of each particle. With the RBFN sampling step, PF-RBF can give an accurate proposal distribution and maintain the convergence of a sensor system. Simulation results verify that PF-RBF performs better than the Unscented Kalman Filter (UKF), PF and Unscented Particle Filter (UPF) in both robustness and accuracy whether the observation model used for the sensor system is linear or nonlinear. Moreover, the intrinsic property of PF-RBF determines that, when the particle number exceeds a certain amount, the execution time of PF-RBF is less than UPF. This makes PF-RBF a better candidate for the sensor systems which need many particles for target tracking. Molecular Diversity Preservation International (MDPI) 2007-01-29 /pmc/articles/PMC3756717/ Text en © 2007 by MDPI (http://www.mdpi.org). Reproduction is permitted for noncommercial purposes.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Wang, Xue
Wang, Sheng
Ma, Jun-Jie
spellingShingle Wang, Xue
Wang, Sheng
Ma, Jun-Jie
An Improved Particle Filter for Target Tracking in Sensor Systems
author_facet Wang, Xue
Wang, Sheng
Ma, Jun-Jie
author_sort Wang, Xue
title An Improved Particle Filter for Target Tracking in Sensor Systems
title_short An Improved Particle Filter for Target Tracking in Sensor Systems
title_full An Improved Particle Filter for Target Tracking in Sensor Systems
title_fullStr An Improved Particle Filter for Target Tracking in Sensor Systems
title_full_unstemmed An Improved Particle Filter for Target Tracking in Sensor Systems
title_sort improved particle filter for target tracking in sensor systems
description Sensor systems are not always equipped with the ability to track targets. Sudden maneuvers of a target can have a great impact on the sensor system, which will increase the miss rate and rate of false target detection. The use of the generic particle filter (PF) algorithm is well known for target tracking, but it can not overcome the degeneracy of particles and cumulation of estimation errors. In this paper, we propose an improved PF algorithm called PF-RBF. This algorithm uses the radial-basis function network (RBFN) in the sampling step for dynamically constructing the process model from observations and updating the value of each particle. With the RBFN sampling step, PF-RBF can give an accurate proposal distribution and maintain the convergence of a sensor system. Simulation results verify that PF-RBF performs better than the Unscented Kalman Filter (UKF), PF and Unscented Particle Filter (UPF) in both robustness and accuracy whether the observation model used for the sensor system is linear or nonlinear. Moreover, the intrinsic property of PF-RBF determines that, when the particle number exceeds a certain amount, the execution time of PF-RBF is less than UPF. This makes PF-RBF a better candidate for the sensor systems which need many particles for target tracking.
publisher Molecular Diversity Preservation International (MDPI)
publishDate 2007
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3756717/
_version_ 1612006762903240704

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