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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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Academic Editor: Stefano Mariani
Algorithms 2015, 8(4), 965-981; https://0-doi-org.brum.beds.ac.uk/10.3390/a8040965
Received: 19 August 2015 / Revised: 24 October 2015 / Accepted: 27 October 2015 / Published: 3 November 2015
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
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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. https://0-doi-org.brum.beds.ac.uk/10.3390/a8040965

AMA Style

Zhang C, Li L, Wang Y. A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution. Algorithms. 2015; 8(4):965-981. https://0-doi-org.brum.beds.ac.uk/10.3390/a8040965

Chicago/Turabian Style

Zhang, Chaozhu; Li, Lin; Wang, Yu. 2015. "A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution" Algorithms 8, no. 4: 965-981. https://0-doi-org.brum.beds.ac.uk/10.3390/a8040965

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