基于MMPHDF的多机动目标联合检测、跟踪与分类算法

针对场景中存在新目标出现、旧目标消失(即目标数目变化)和密集杂波的复杂情形,利用多模型概率假设密度滤波器(MMPHDF)在多机动目标联合检测与跟踪上的优势,加入类别辅助信息,提出了一种多机动目标联合检测、跟踪与分类算法.该算法的基本思想是在MMPHDF中用属性向量扩展单目标状态向量,用位置和属性的组合测量似然函数代替单目标位置及杂波位置测量似然函数,提高了不同类目标与杂波测量间的鉴别能力,从而改善了目标数目及状态的估计精度;在更新目标状态后,对目标属性信息进行更新,更为精确的目标数目及状态估计又保证了目标分类性能.本文给出了该算法的粒子实现方法.仿真结果验证了上述结论.

Joint detection, tracking and classification algorithm of multiple maneuvering targets using MMPHDF

To account for joint detection, tracking and classification (JDTC) of multiple maneuvering targets in dense clutter environment, this paper introduces an algorithm based on the multiple-model probability hypothesis density filter (MMPHDF). The MMPHDF can be applied to jointly detect and track multiple maneuvering targets, but its filter performance deteriorates rapidly in dense clutter environment. The proposed JDTC algorithm (MMPHDF-JDTC) utilizes target classification information and target kinematic information to simultaneously estimate the time-varying number of targets, their kinematic states and types. The main idea is to augment the kinematic state vector with the target class vector, and then use their combined measurement likelihood to integrate the target classification information into the update process of MMPHDF. The combined target kinematic state and class measurement likelihood improves the discrimination of different class targets and clutter, so better detection and tracking performance can be expected compared with the original MMPHDF. Alternately, accurate detection and tracking results is the foundation for correct target classification. A particle implementation of the MMPHDF-JDTC has been given. Simulation results validate the above conclusions.