多机动目标跟踪的IMM-GMPHD滤波算法

针对现有多机动目标跟踪算法精度低、计算量大、约束条件苛刻等问题，本文将高斯混合概率假设密度（Gaussian Mixture PHD，GM-PHD）滤波器和交互式多模型（Interacting Multiple Model，IMM）相结合，提出交互式多模型GM-PHD（Interacting Multiple Model GMPHD，IMM-GMPHD）滤波算法。算法不仅避免了多目标跟踪中的数据关联问题，而且在漏检、目标密集、目标机动、航迹交叉、目标数目未知的杂波环境下能够稳定、精确地估计目标数目和状态。100次蒙特卡洛（Monte Carlo，MC）仿真结果表明，IMM-GMPHD滤波器能在不增加额外计算负担的基础上，体现出较高的精确度和较强的鲁棒性。

IMM-GMPHD Filter for Multiple Maneuvering Targets Tracking

Considering the traditional data association algorithm of multiple maneuvering targets tracking being of hard constraint condition, lower estimated accuracy, and higher computational complexity. In this paper an interacting multiple-model (IMM) implementation of the GMPHD filter is proposed to estimate the states of multiple targets which may have different maneuverability in measurements system. The new algorithm avoids the difficult problem of data association. It is able to deal with clutter, miss detection, false alarm, dense, and cross targets tracking effectively. 100 Monte Carlo (MC) simulation results show that the proposed MM-GMPHD filter significantly outperforms in estimating the number and states of the multiple maneuvering targets. The proposed algorithm has higher tracking accuracy and more steady tracking performance.