Abstract
Extended object tracking involves estimating both the physical extent and kinematic parameters of a target object, where typically multiple measurements are observed per time step. In this article, we propose a deterministic closed-form elliptical extended object tracker, based on decoupling of the kinematics, orientation, and axis lengths. By disregarding potential correlations between these state components, fewer approximations are required for the individual estimators than for an overall joint solution. The resulting algorithm outperforms existing algorithms, reaching the accuracy of sampling-based procedures. Additionally, a batch-based variant is introduced, yielding highly efficient computation while outperforming all comparable state-of-the-art algorithms. This is validated both by a simulation study using common models from literature, as well as an extensive quantitative evaluation on real automotive radar data.
Abstract (translated)
扩展目标跟踪涉及估算目标对象的物理范围和运动学参数，通常在每个时间步骤中会观察到多个测量值。本文提出了一种基于确定性闭式椭圆扩展目标追踪器，该方法通过分离运动学、姿态和轴长来实现。通过忽略这些状态组件之间的潜在相关性，对于单独的估计器比总体联合解决方案所需的近似更少。由此产生的算法优于现有的算法，并且达到了采样法程序的精度水平。此外，还引入了一种基于批处理的方法，该方法计算效率极高，并在所有同类最先进的算法中表现最佳。这一结论既通过使用文献中的常见模型进行仿真研究得到验证，也通过大量的定量评估实际汽车雷达数据得到了证实。
URL
https://arxiv.org/abs/2512.14426
