Multiple extended target tracking (ETT) has gained increasing attention due to the development of high-precision LiDAR and radar sensors in automotive applications. For LiDAR point cloud-based vehicle tracking, this paper presents a probabilistic measurement-region association (PMRA) ETT model, which can describe the complex measurement distribution by partitioning the target extent into different regions. The PMRA model overcomes the drawbacks of previous data-region association (DRA) models by eliminating the approximation error of constrained estimation and using continuous integrals to more reliably calculate the association probabilities. Furthermore, the PMRA model is integrated with the Poisson multi-Bernoulli mixture (PMBM) filter for tracking multiple vehicles. Simulation results illustrate the superior estimation accuracy of the proposed PMRA-PMBM filter in terms of both positions and extents of the vehicles comparing with PMBM filters using the gamma Gaussian inverse Wishart and DRA implementations.

翻译：多扩展目标跟踪（ETT）因高精度激光雷达与雷达传感器在汽车领域的发展而日益受到关注。针对基于激光雷达点云的车辆跟踪，本文提出了一种概率测量区域关联（PMRA）ETT模型，该模型通过将目标范围划分为不同区域来描述复杂测量分布。PMRA模型克服了先前数据区域关联（DRA）模型的缺陷，消除了约束估计的近似误差，并利用连续积分更可靠地计算关联概率。此外，该PMRA模型与泊松多伯努利混合（PMBM）滤波器相结合，用于多车辆跟踪。仿真结果表明，与采用伽马高斯逆威沙特和DRA实现的PMBM滤波器相比，所提出的PMRA-PMBM滤波器在车辆位置和范围估计精度方面均具有更优性能。