Gm-Phd Filter with State-Dependent Clutter

In traditional filtering methods, clutter is often assumed to obey a uniform distribution over the entire monitoring area. For many sensors, however, clutter may concentrate in target-containing regions. Under this condition, the performance of the traditional multi-target tracking filter can be degraded. In an effort to solve this problem, this paper proposes an improved algorithm based on a Gaussian Mixture probability hypothesis density (GM-PHD) filter to deal with state-dependent clutter. First, the relationship between state and clutter is modeled using the uniform distribution centered on the target state. Then, the clutter intensity is calculated according to the distribution of clutter in the whole monitoring area and is used to update the filter. The simulation results show that the improved filter can track targets' trajectories more effectively in an environment of state-dependent clutter than the standard GM-PHD filter.