This paper presents two new trajectory probability hypothesis density (TPHD) and trajectory cardinality probability hypothesis density (TCPHD) filters for joint tracking and classification (JTC), namely JTC-TPHD and JTC-TCPHD filters. We first introduce the classified trajectory RFS model to accommodate the motion model-based class information. The adaptation of the TPHD and TCPHD filters to the classified trajectory RFS is then formulated, i.e., JTC-TPHD and JTC-TCPHD, which are capable of jointly estimating the number, class, motion model, and trajectory status of multiple targets. We also develop the linear Gaussian implementation (LGM) as an analytic closed-form solution of the proposed filters. Simulation experiments discuss the validity and superiority of the proposed filters for multi-target JTC.

中文翻译：

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使用 TPHD 和 TCPHD 滤波器的基于运动模型的联合跟踪和分类

本文提出了两种用于联合跟踪和分类（JTC）的新轨迹概率假设密度（TPHD）和轨迹基数概率假设密度（TCPHD）滤波器，即JTC-TPHD和JTC-TCPHD滤波器。我们首先引入分类轨迹 RFS 模型来容纳基于运动模型的类别信息。然后制定了TPHD和TCPHD滤波器对分类轨迹RFS的适应，即JTC-TPHD和JTC-TCPHD，它们能够联合估计多个目标的数量、类别、运动模型和轨迹状态。我们还开发了线性高斯实现（LGM）作为所提出的滤波器的解析封闭式解决方案。仿真实验讨论了所提出的多目标 JTC 滤波器的有效性和优越性。