  This paper 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 of this filter, where Gaussian densities correspond
to point targets and Gamma Gaussian Inverse Wishart densities for extended
targets. The L-scan approximation is also proposed as a simplified version to
mitigate the huge computational cost. Simulation and experimental results show
that the proposed filter is able to classify targets correctly and obtain
accurate trajectory estimation.
