The Trajectory PHD Filter for Coexisting Point and Extended Target Tracking

Abstract

This article develops a general trajectory probability hypothesis density (TPHD) filter, which uses a general density for target-generated measurements and is able to estimate trajectories of coexisting point and extended targets. First, we provide a derivation of this general TPHD filter based on finding the best Poisson posterior approximation by minimizing the Kullback–Leibler divergence, without using probability generating functionals. Second, we adopt an efficient implementation for this filter, where Gaussian densities correspond to point targets and gamma Gaussian inverse Wishart densities for extended targets. Simulation and experimental results show that the proposed filter is able to classify targets correctly and obtain accurate trajectory estimation.