基于MMPHDF 的多机动目标联合检测、跟踪与分类算法

Abstract

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.