SMC-PHD based multi-target track-before-detect with nonstandard point observations model

Abstract

Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio (SNR). A modified multi-target track-before-detect (TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo (SMC)-based probability hypothesis density (PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.

Keywords

adaptive particle sampling / multi-target track-before-detect / probability hypothesis density (PHD) filter / sequential Monte Carlo (SMC) method

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