A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution

A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution

In this paper, we address the problem of detecting and tracking targets with a low signal-to-noise ratio (SNR) by exploiting hybrid differential evolution (HDE) in the particle filter track-before-detect (PF-TBD) context. Firstly, we introduce the Bayesian PF-TBD method and its weaknesses. Secondly...

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Bibliographic Details
Main Authors: Chaozhu Zhang, Lin Li, Yu Wang
Format: Article
Language:English
Published: MDPI AG 2015-11-01
Series:Algorithms
Subjects:
track-before-detect
particle filter
hybrid differential evolution
Online Access:http://www.mdpi.com/1999-4893/8/4/965
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spelling doaj-b1b77d286ce248c1b4bc9e6d57af95762020-11-24T22:17:01ZengMDPI AGAlgorithms1999-48932015-11-018496598110.3390/a8040965a8040965A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential EvolutionChaozhu Zhang0Lin Li1Yu Wang2Department of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaDepartment of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaDepartment of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaIn this paper, we address the problem of detecting and tracking targets with a low signal-to-noise ratio (SNR) by exploiting hybrid differential evolution (HDE) in the particle filter track-before-detect (PF-TBD) context. Firstly, we introduce the Bayesian PF-TBD method and its weaknesses. Secondly, the HDE algorithm is regarded as a novel particle updating strategy, which is proposed to optimize the performance of the PF-TBD algorithm. Thirdly, we combine the systematic resampling approach to enhance the performance of the proposed algorithm. Then, an improved PF-TBD algorithm based on the HDE method is proposed. Experiment results indicate that the proposed method has better performance in detecting and tracking than previous algorithms when the targets have a low SNR.http://www.mdpi.com/1999-4893/8/4/965track-before-detectparticle filterhybrid differential evolution
collection DOAJ
language English
format Article
sources DOAJ
author Chaozhu Zhang
Lin Li
Yu Wang
spellingShingle Chaozhu Zhang
Lin Li
Yu Wang
A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution
Algorithms
track-before-detect
particle filter
hybrid differential evolution
author_facet Chaozhu Zhang
Lin Li
Yu Wang
author_sort Chaozhu Zhang
title A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution
title_short A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution
title_full A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution
title_fullStr A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution
title_full_unstemmed A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution
title_sort particle filter track-before-detect algorithm based on hybrid differential evolution
publisher MDPI AG
series Algorithms
issn 1999-4893
publishDate 2015-11-01
description In this paper, we address the problem of detecting and tracking targets with a low signal-to-noise ratio (SNR) by exploiting hybrid differential evolution (HDE) in the particle filter track-before-detect (PF-TBD) context. Firstly, we introduce the Bayesian PF-TBD method and its weaknesses. Secondly, the HDE algorithm is regarded as a novel particle updating strategy, which is proposed to optimize the performance of the PF-TBD algorithm. Thirdly, we combine the systematic resampling approach to enhance the performance of the proposed algorithm. Then, an improved PF-TBD algorithm based on the HDE method is proposed. Experiment results indicate that the proposed method has better performance in detecting and tracking than previous algorithms when the targets have a low SNR.
topic track-before-detect
particle filter
hybrid differential evolution
url http://www.mdpi.com/1999-4893/8/4/965
work_keys_str_mv AT chaozhuzhang aparticlefiltertrackbeforedetectalgorithmbasedonhybriddifferentialevolution
AT linli aparticlefiltertrackbeforedetectalgorithmbasedonhybriddifferentialevolution
AT yuwang aparticlefiltertrackbeforedetectalgorithmbasedonhybriddifferentialevolution
AT chaozhuzhang particlefiltertrackbeforedetectalgorithmbasedonhybriddifferentialevolution
AT linli particlefiltertrackbeforedetectalgorithmbasedonhybriddifferentialevolution
AT yuwang particlefiltertrackbeforedetectalgorithmbasedonhybriddifferentialevolution
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