Improved SMC-PHD Filter for Multi-Target Track-Before-Detect

摘要

The Sequential Monte Carlo Probability Hypothesis Density (SMC-PHD) filter with the idea of track- before-detect (TBD) is a kind of effective means to deal with multitarget detection and tracking under complex electromagnetic condition with low Signal- to-Noise Ratio (SNR). However, it suffers from inaccurate estimation number of targets and large computational complexity due to the improper assumption when the TBD is incorporated into the SMC-PHD filter. To combat this problem, we propose an improved SMC-PHD filter method for multitarget TBD in this paper, where a new measurement model and a novel method to determine the clutter density are designed. Simulation results demonstrate that the proposed method outperforms the traditional method in terms of tracking accuracy and computational complexity.