用冯·米塞斯分布提升雷达目标方向估计的不确定性量化精度。
Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets
- 采用冯·米塞斯分布建模方向估计的不确定性，符合角度数据几何特性。
- 在正常条件下不确定性更低，对严重干扰更敏感，鲁棒性更强。
- 可直接用于跟踪模块的联合概率融合，适合自动驾驶系统集成。
本文研究了面向车载雷达方向到达（DOA）估计的不确定性感知深度学习方法，重点在于概率建模与下游集成。比较了基于圆统计学的冯·米塞斯（VM）集成（ENS）与基于正态逆伽马的证据深度学习（EDL）框架，后者在欧氏空间中生成学生t分布预测结果。ENS框架以（μ, κ）参数化角度预测，提供与方向几何一致的可解释不确定性。在分布内及多种分布外条件下，通过风险覆盖率和ROC/AUROC分析评估性能。结果表明，ENS在正常条件下不确定性更低，对严重扰动更具敏感性；而EDL则表现出更平滑的不确定性变化和略优的排序一致性。尤为重要的是，ENS表示可通过闭式冯·米塞斯似然直接集成至关联模块，实现统一的检测-跟踪流水线。这些发现揭示了不确定性感知DOA估计中几何一致性与统计普适性之间的权衡。
原文摘要 · Abstract (English)
This work investigates uncertainty-aware deep learning approaches for direction of arrival (DOA) estimation in automotive radar, focusing on probabilistic modeling and downstream integration. A circular-statistics-based von Mises (VM) ensemble (ENS) is compared with an evidential deep learning (EDL) framework based on a normal inverse gamma formulation, yielding a Student t predictive distribution in the Euclidean domain. The ENS framework produces angular predictions parameterized by (mu, kappa), enabling interpretable uncertainty aligned with directional geometry. Performance is evaluated under in distribution and multiple out-of-distribution conditions using risk coverage and ROC or AUROC analyses. Results indicate that ENS achieves lower uncertainty under nominal conditions and exhibits stronger sensitivity to severe perturbations, whereas EDL provides smoother uncertainty variation and slightly improved ranking consistency. Importantly, the ENS representation enables direct probabilistic integration into association modules via closed form VM likelihoods, facilitating a unified detection tracking pipeline. These findings highlight a trade-off between geometric consistency and statistical generality in uncertainty-aware DOA estimation.
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