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.

翻译：扩展目标跟踪涉及同时估计目标物体的物理范围与运动学参数，通常每个时间步会观测到多个测量值。本文提出一种基于运动学、朝向及轴长分量解耦的确定性闭式椭圆扩展目标跟踪器。通过忽略这些状态分量间可能的相关性，相较于整体联合解算方案，各独立估计器所需的近似处理更少。所提算法性能优于现有方法，达到了基于采样方法的精度水平。此外，本文还引入了批处理变体，在计算效率显著提升的同时，其性能超越所有可比的先进算法。该结论通过采用文献常见模型的仿真研究，以及基于真实车载雷达数据的广泛定量评估得到了验证。