PHD Filtering with target amplitude feature

Abstract

In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multi-target state estimation by obtaining more accurate target and false alarm likelihoods. Target amplitude feature is well know to improve data association in conventional tracking filters (such as the PDA, MHT), and results in better tracking performance of low SNR targets. The advantage of using the target amplitude approach is that targets can be identified earlier through the enhanced discrimination between target and false alarms. We illustrate this approach in the context of multiple targets of unknown and different signal to noise ratios in the framework of the Probability Hypothesis Density filter. The simulation results demonstrate the significant improvement in performance particularly in the estimate of the number of targets.