一种新的多机动目标跟踪的GMPHD滤波算法

针对多机动目标跟踪的传统数据关联算法约束条件苛刻、估计精度低、计算量大等问题,提出了一种基于随机集理论的非数据关联的多机动目标跟踪算法.该算法将高斯混合概率假设密度(GMPHD)滤波与"当前"统计模型的优点相结合,绕过了棘手的数据关联问题,能高效处理目标数较大的机动跟踪问题.在漏检、虚警、多机动目标交叉杂波复杂环境下进行了仿真实验,结果表明,该算法具有较高的跟踪精度和稳健的跟踪性能.

A New GMPHD Filter Algorithm 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, a non data association tracking algorithm based on the random set theory was proposed. Since the proposed algorithm integrates the both advantages of Gaussian mixture probability hypothesis density (GMPHD) filter and current statistical mode1, avoids the difficult problem of data association, it is able to deal with multiple maneuvering targets tracking effectively. A simulation experiment was performed in the complex environment with clutter, miss detection, false alarm, dense, and cross targets. The simulation results show that the proposed algorithm has higher tracking accuracy and more steady tracking performance.