A PHD-Based Particle Filter for Detecting and Tracking Multiple Weak Targets

Joint detection and tracking weak target is a challenging problem whose complexity is intensified when there are multiple targets present at the same time. Some Probability Hypothesis Density (PHD) based track-before-detect (TBD) particle filters (PHD-TBD) are proposed to solve this issue; however, the performance is unsatisfactory especially when the number of targets is large because some assumptions in PHD are violated. We propose to modify the general PHD-TBD filter in two aspects to make the PHD processing available for TBD scenarios. First, the distribution of false alarms is approximated as the Poisson distribution through a threshold method, and then a clustering technique is proposed to solve the overestimation of the target number. A typical TBD scenario is used to test the effectiveness of the proposed method. Simulation results indicate that the proposed method outperforms the general method in terms of estimation accuracy and computational complexity.