分布式边缘设备上基于物理信息的到达方向估计
Physics-Informed Direction-of-Arrival Estimation Over Distributed Edge Devices
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中文总结 AI 辅助
研究分布式边缘设备上到达方向估计问题，提出物理信息联邦学习框架，通过流形感知正则化将导向矢量几何纳入局部训练目标，优于多个联邦学习基线方法。
中文摘要 AI 辅助
到达方向（DoA）估计是一项基本的阵列处理任务，深度学习使其受益匪浅。在分布式边缘设备上部署此类方法会带来隐私和通信限制，联邦学习（FL）可解决这些问题。然而，标准FL算法将DoA视为一般分类问题，忽略了阵列流形的潜在物理特性。为解决此问题，我们提出一种用于DoA估计的物理信息FL框架，该框架通过流形感知正则化将导向矢量几何直接纳入局部训练目标。与现有的FL基线不同，我们框架中的正则化惩罚导向空间中的差异而非标签空间中的差异，利用了阵列流形的已知几何结构。我们为我们的框架提供了理论收敛保证，表明其收敛到一个驻点。仿真结果证实，我们的物理信息方法在独立同分布和非独立同分布数据条件下均优于多种FL基线方法。
英文摘要
Direction-of-arrival (DoA) estimation is a fundamental array processing task that has benefited substantially from deep learning. Deploying such methods using data distributed across multiple edge devices introduces communications overhead, due to the aggregation of large datasets that federated learning (FL) can address. Yet, standard FL algorithms treat DoA as a generic classification problem, ignoring the underlying physics of the array manifold. To address this, we propose a physics-informed FL framework for DoA estimation that incorporates steering-vector geometry directly into the local training objective via a manifold-aware regularizer. Unlike existing FL baselines, the regularizer in our framework penalizes discrepancies in steering space rather than label space, exploiting the known geometric structure of the array manifold. We provide theoretical convergence guarantees for our framework, showing convergence to a stationary point. Simulation results confirm that our physics-informed approach outperforms multiple FL baseline approaches across iid and non-iid data conditions.
发表机构
- University of California San Diego（加州大学圣地亚哥分校）
机构由 AI 辅助整理，请以论文原文为准。
