When the number of targets is unknown or varies with time, multi-target state and measurements are represented
as random sets and the multi-target tracking problem is addressed by calculating the probability hypothesis density(PHD) of
the joint distribution, recursively. However, PHD can not provide a closed-form solution to the nonlinear problem occurred in
the passive bearings-only multi-target tracking system. A new Gaussian mixture particle PHD(GMPPHD) filter is presented
in the paper. The PHD is approximated by a mixture of Gaussians, which avoids clustering to determine target states. And
Quasi-Monte Carlo integration method is used for approximating the prediction and update distributions of target states.
Simulation results show the effectiveness of the proposed algorithm.