发表机构
ECE School, Ben-Gurion University of the Negev（内盖夫本-古里安大学电子与计算机工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种AI辅助ESPRIT框架，结合模型驱动深度学习与经典子空间分析，实现联合DoA估计与不确定性量化，在相干源、有限快拍等挑战场景下保持准确性与可靠性。
AI 中文摘要
到达方向（DoA）估计通常不仅需要准确恢复信号源方向，还需要对估计中的不确定性进行可靠表征。虽然经典子空间方法如ESPRIT提供了原理性的不确定性分析，但其性能和不确定性量化依赖于严格的假设。近期深度学习方法缓解了这些限制，并能在挑战性条件下实现稳健的DoA估计，但通常仅提供点估计，缺乏原理性的不确定性表征。在本工作中，我们开发了一个用于联合DoA估计与不确定性量化的AI辅助框架，该框架结合了基于模型的深度学习的稳健性与经典子空间方法的分析基础。基于AI代理协方差恢复，我们扩展了现有ESPRIT不确定性分析，以表征DoA估计误差的完整协方差结构，并将此表征集成到面向子空间的深度学习架构中。我们进一步提出了一种专门的学习策略，该策略联合促进准确的DoA恢复和忠实的不确定性估计。所提出的方法保留了经典子空间方法的可解释处理流程，同时能在传统方法难以应对的场景中实现可靠运行。我们的数值研究表明，所提出的框架在多种挑战性场景（包括相干源、有限快拍数和阵列校准误差）中，始终能实现准确的DoA估计以及可靠的不确定性表征。
英文摘要
DoA estimation often requires not only accurate recovery of source directions, but also reliable characterization of the uncertainty in these estimates. While classical subspace methods such as ESPRIT provide principled uncertainty analyses, their performance and uncertainty quantification rely on restrictive assumptions. Recent deep learning approaches alleviate these limitations and enable robust. DoA estimation in challenging conditions, but generally provide point estimates and lack principled uncertainty characterization. In this work, we develop an AI-aided framework for joint DoA estimation and uncertainty quantification that combines the robustness of model-based deep learning with the analytical foundations of classical subspace methods. Building on AI surrogate covariance recovery, we extend existing ESPRIT uncertainty analyses to characterize the full covariance structure of the DoA estimation error and integrate this characterization into a subspace-oriented deep learning architecture. We further propose a dedicated learning strategy that jointly promotes accurate DoA recovery and faithful uncertainty estimation. The resulting methodology preserves the interpretable processing pipeline of classical subspace methods while enabling reliable operation in regimes where conventional approaches struggle. Our numerical studies demonstrate that the proposed framework consistently achieves accurate DoA estimation together with reliable uncertainty characterization across diverse challenging scenarios, including coherent sources, limited snapshots, and array calibration errors.
Comments13 Pages, 11 Figures
