Likelihood-gating Sequential Monte Carlo Probability Hypothesis Density Filter

doi:10.3969/j.issn.1006-2475.2017.11.020

Abstract

Abstract: To resolve the low computational efficiency of the sequential Monte Carlo (SMC) implementation of the probability hypothesis density (PHD) filter, which requires a large number of particles, we propose an improved SMC-PHD filter called the likelihood-gating SMC-PHD filter. Firstly, based on all the predicted particles, the maximum number of actual surviving observations can be selected, as all multi-target posterior information is utilized. Secondly, based on all the likelihood values of predicted particles in the updater, the proposed filter can be easily implemented in various applications, as it obviates labeling the particles and calculating the distances. Only the effective observations are employed to update the weight of particles. Experiments show that this filter has excellent real-time performance and better filtering accuracy compared with the basic SMC-PHD filter.

Key words: multi-target tracking, probability hypothesis density filter, sequential Monte Carlo, gating, rejecting clutter

CLC Number:

TP391

GAO Yi-yue1,2, JIANG De-fu2, LIU Ming2, FU Wei2. Likelihood-gating Sequential Monte Carlo Probability Hypothesis Density Filter[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.11.020.

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