Adaptive Gaussian mixture probability hypothesis density for tracking multiple targets

The probability hypothesis density (PHD) filter is a promising method for multi-target tracking problem. The Gaussian mixture PHD filter is an analytic solution to the PHD filter for linear Gaussian multi-target models. However, the PHD filter is inapplicable to the multi-target tracking scenario with unknown newborn target intensity. To overcome the problem, an adaptive Gaussian mixture probability hypothesis density algorithm for multiple target tracking is proposed, where the Dirichlet distribution with negative exponent parameters and target maximum velocity constraint-based schemes are introduced to recursively estimate the newborn target intensity at each time step. The simulation results illustrate that the proposed algorithm has better performance under the unknown newborn target intensity in the multi-target tracking systems.