解耦状态分量，实现高效高精度椭圆目标跟踪
Decoupled Quadratic Kalman Filter for Elliptical Extended Object Tracking with Log-normal Axis Modeling
- 解耦运动、方向和轴长，降低估计复杂度
- 采用对数正态建模，确保轴长恒为正
- 计算效率高，实测雷达数据表现最优
扩展目标跟踪需同时估计目标的物理尺寸与运动参数，通常每时刻有多组测量。本文提出一种确定性闭式椭圆扩展目标追踪器，通过解耦运动、方向与轴长状态分量，减少各子估计器的近似依赖。该方法支持半轴长度的对数正态建模，确保轴长始终为正。所提算法在性能上达到采样类方法水平，且计算效率更高；进一步引入批处理变体，在真实汽车雷达数据上的定量评估中优于所有现有先进算法，仿真验证亦充分。
原文摘要 · Abstract (English)
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. This also enables a log-normal representation of the semi-axis lengths, i.e., opposed to related approaches, only positive axis lengths have support. 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.
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