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This paper develops a general trajectory probability hypothesis density(TPHD) filter, which uses a general density for target-generated measurementsand is able to estimate trajectories of coexisting point and extended targets.First, we provide a derivation of this general TPHD filter based on finding thebest Poisson posterior approximation by minimizing the Kullback-Leiblerdivergence, without using probability generating functionals. Second, we adoptan efficient implementation of this filter, where Gaussian densities correspondto point targets and Gamma Gaussian Inverse Wishart densities for extendedtargets. The L-scan approximation is also proposed as a simplified version tomitigate the huge computational cost. Simulation and experimental results showthat the proposed filter is able to classify targets correctly and obtainaccurate trajectory estimation.

The Trajectory PHD Filter for Coexisting Point and Extended Target Tracking