The Gaussian Mixture implementation of Probability Hypothesis Density Filter in Linear Gaussian Jump Markov multi-target System model (LGJMS-GMPHDF) is proved to be an effective tool for tracking an unknown and time-varying number of targets with uncertain target dynamics in clutter. This paper further integrates the class information into LGJMS-GMPHDF and proposes a recursive Joint Detection Tracking and Classification (JDTC) algorithm for multiple maneuvering targets in dense clutter. The main idea is to augment the kinematic state vector with the target class vector, and then use their combined measurement likelihood to integrating the target classification information into the update process of LGJMS-GMPHDF. 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 LGJMS-GMPHDF. The classification probabilities and state vectors are updated synchronously. The proposed JDTC algorithm can simultaneously estimate the time-varying number of maneuvering target, their corresponding kinematic states and classes. The algorithm is demonstrated via a simulation example involving tracking of two closely spaced parallel moving targets and two crossing moving targets from different classes, where targets can appear and disappear.%线性高斯跳变马尔可夫系统模型下的高斯混合概率假设密度滤波器(LGJMS-GMPHDF)为杂波背景下多机动目标跟踪提供了一种有效方法.该文将类别辅助信息引入LGJMS-GMPHDF,提出了一种密集杂波背景下多机动目标联合检测、跟踪与分类算法.该算法在LGJMS-GMPHDF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,进而改善了目标数目及状态的估计精度；在更新目标状态的同时,对目标属性信息进行更新.该算法实现了时变数目的目标状态和类别估计.杂波背景下交叉和临近并行机动目标的跟踪实验验证了该文算法的联合检测、跟踪与分类性能.
展开▼
