基于冯·米塞斯分布的密集汽车雷达目标不确定性量化
Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets
AI总结：
针对汽车雷达波达方向估计，比较了基于冯·米塞斯分布的集成学习与基于正态逆伽马的证据深度学习的不确定性量化方法，发现集成学习在几何一致性和扰动敏感性上更优，且可直接集成到跟踪模块。
摘要：
本工作研究了用于汽车雷达波达方向（DOA）估计的不确定性感知深度学习方法，重点关注概率建模和下游集成。基于圆形统计的冯·米塞斯（VM）集成（ENS）与基于正态逆伽马公式的证据深度学习（EDL）框架进行了比较，后者在欧几里得域产生学生t预测分布。ENS框架产生由(mu, kappa)参数化的角度预测，实现与方向几何对齐的可解释不确定性。使用风险覆盖和ROC或AUROC分析，在分布内和多种分布外条件下评估性能。结果表明，ENS在标称条件下实现更低的不确定性，并对严重扰动表现出更强的敏感性，而EDL提供更平滑的不确定性变化和略微更好的排序一致性。重要的是，ENS表示通过封闭形式的VM似然实现直接概率集成到关联模块中，促进统一的检测跟踪流水线。这些发现突出了不确定性感知DOA估计中几何一致性与统计通用性之间的权衡。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
