非椭圆扩展目标联合跟踪与分类算法

充分利用目标尺寸和形状信息,提出了一种基于星凸随机超曲面模型(random hypersurface model, RHM)的非椭圆扩展目标联合跟踪与分类(joint tracking and classification, JTC)算法。将目标空间扩展状态建模为星凸形状,通过目标类别相关先验信息的矢量化建模,建立起其与目标瞬时扩展状态的关系,并在统一的贝叶斯滤波框架下,实现跟踪与分类的一体化处理；进一步对目标运动学状态和扩展状态单独进行建模,并通过构建扩展状态的似然函数,利用粒子滤波实现目标类别概率算式的递推处理。仿真结果表明:与基于椭圆形状的扩展目标JTC算法相比,所提算法能对尺寸相近、形状不同的目标进行准确分类,同时可改善目标状态的估计效果；与基于星凸RHM的扩展目标跟踪算法相比,所提算法能大幅提高目标状态的估计性能。

Joint tracking and classification algorithm of non-ellipsoidal extended target

By making full use of the target size and shape information, a NEET (non-ellipsoidal extended target) JTC (joint tracking and classification) algorithm was proposed on the basis of the star-convex RHM (random hypersurface model). In the proposed algorithm, the target extent state was modeled as star-convex shape. By modeling the target class-related prior information with vector form, constructing its relationship with the simultaneous extent state, and integrating it into the framework of Bayesian filter, the joint processing of tracking and classification was realized. Additionally, two separate vectors were used to model the target state, and the probability update of target class was realized by particle filter based on likelihood function derivation. The simulation results show that the NEET JTC algorithm can accurately classify targets with similar size but different shapes, and improve the target state estimation results when compared with the extended target JTC algorithm based on elliptical shape. The results also show that the proposed algorithm can significantly improve the target state estimation performance when compared with the extended target tracking algorithm based on star-convex RHM.