Probability hypothesis density filter with imperfect detection probability for multi-target tracking

Abstract

Probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data association between measurements and targets. However, the PHD filter cannot be directly applied to track targets in imperfect detection probability conditions. Otherwise, the performance of almost all the PHD-based filters significantly decreases. Aiming at improving the estimate accuracy as for target states and their number, a multi-target tracking algorithm using the probability hypothesis density filter is proposed, where a novel multi-frame scheme is introduced to cope with estimates of undetected targets caused by the imperfect detection probability. According to the weights of targets at different time steps, both the previous weight array and state extraction identifier of individual targets are constructed. When the targets are undetected at some times, the states of the undetected targets are extracted based on previous weight arrays and state extraction identifiers of correlative targets. Simulation results show that the proposed algorithm effectively improves the performance of the existing relevant PHD-based filters in imperfect detection of probability scenarios.