Iterative update correction and multi-frame state extraction based probability hypothesis density filter

Abstract

Probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data association between measurements and tracks. The Gaussian mixture (GM) implementation of the PHD filter is a closed-form solution to the PHD filter for linear Gaussian model. However, the Gaussian mixture PHD filter suffers from filtering performance degradation problem in multi-target tracking scenarios with low probability of detection, especially when it comes to tracking nearby targets in the imperfect probability of detection conditions. Aiming at the problem, a robust Gaussian mixture PHD algorithm for tracking multiple targets is proposed. First, a novel nearby target tracking method is introduced to reallocate the possible incorrect weights of the nearby targets. Then, a novel target state estimation scheme, making full use of the multiple previous weights of the targets, is adopted to extract the estimates of the target states. Simulation experiments have demonstrated that the proposed approach can achieve better performance in terms of target states and their number than the other related algorithms when tracking multiple nearby targets in the low probability of detection scenarios.