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Algorithms 2015, 8(4), 965-981; doi:10.3390/a8040965

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

Department of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
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Author to whom correspondence should be addressed.
Academic Editor: Stefano Mariani
Received: 19 August 2015 / Revised: 24 October 2015 / Accepted: 27 October 2015 / Published: 3 November 2015
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Abstract

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. View Full-Text
Keywords: track-before-detect; particle filter; hybrid differential evolution track-before-detect; particle filter; hybrid differential evolution
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, C.; Li, L.; Wang, Y. A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution. Algorithms 2015, 8, 965-981.

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