Abstract: For many sensors the clutters observed are easier to concentrate in the area around targets, namely the state-dependent clutter, which is different from the known clutter distribution in traditional filtering algorithms. So, the accuracy the real-time performance and of traditional multi-target tracking will be greatly degraded. To solve this problem, a kind of GM-CPHD filter with state-dependent clutter was proposed. The relationship between clutter and state was modeled. The clutter intensity was recalculated according to the distribution of clutter in the whole surveillance area and applied to update process; at the same time, in order to reduce the time complexity, an adaptive gating strategy was adopted to make a pretreatment, that is, only the measurements which fell into a threshold were used in update process. The simulation results show, in the environment of state-dependent clutter, the proposed algorithm has better filtering accuracy and lower time complexity.

Key words: state-dependent clutter, probability hypothesis density filter, target tracking, clutter intensity, adaptive ellipsoid gating