Active Distributed Sonar Multi-target Tracking Based on SMC-PHD Filtering

doi:10.3969/j.issn.1000-1093.2020.05.013

Abstract

Abstract: An active distributed sonar multi-target automatic tracking method based on sequential Monte Carlo and probability hypothesis density (SMC-PHD) filtering is proposed to solve the problems of large number of clutter, targets uncertainty and observation uncertainty. In the proosed method, the random finite set (RFS) model is used to characterize the target state and observation, and the importance sampling and resampling strategy of sequential Monte Carlo (SMC) method is used to realize the transferring and filtering of probability hypothesis density of multi-target posterior. The multi-target tracking based on SMC-PHD filtering with different number of observation nodes is simulated. The results show that the proposed method can be used to effectively realize multi-target automatic tracking in real time in clutter environment with unknown and time-varying multi-targets. In active distributed sonar system with 4 nodes,the proposed method achieves the high-accuracy tracking with distance estimation relative error less than 0.05 and the completely accurate estimation of targets number. Key

Key words: activedistributedsonar, randomfiniteset, sequentialMonteCarlo, probabilityhypothesisdensityfiltering, multi-targettracking

CLC Number:

TB566

SHAO Pengfei, WANG Lei, WANG Fangyong. Active Distributed Sonar Multi-target Tracking Based on SMC-PHD Filtering[J]. Acta Armamentarii, 2020, 41(5): 941-949.

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第41卷第5期2020 年5月
兵工学报ACTA ARMAMENTARII
Vol.41No.5May2020

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