Robust Covariance-Based DoA Estimation under Weather-Induced Distortion

TL;DR

This paper presents a covariance-based method for robust DoA estimation in adverse weather, modeling rain-induced distortions and exploiting covariance matrix structure to improve accuracy.

Contribution

It introduces a physics-based statistical model for weather distortions and a GLS calibration method that enhances DoA estimation robustness under rain conditions.

Findings

Effective suppression of rain-induced distortions

Improved DoA estimation accuracy in simulations

Enhanced radar sensing performance in adverse weather

Abstract

We investigate robust direction-of-arrival (DoA) estimation for sensor arrays operating in adverse weather conditions, where weather-induced distortions degrade estimation accuracy. Building on a physics-based $S$-matrix model established in prior work, we adopt a statistical characterization of random phase and amplitude distortions caused by multiple scattering in rain. Based on this model, we develop a measurement framework for uniform linear arrays (ULAs) that explicitly incorporates such distortions. To mitigate their impact, we exploit the Hermitian Toeplitz (HT) structure of the covariance matrix to reduce the number of parameters to be estimated. We then apply a generalized least squares (GLS) approach for calibration. Simulation results show that the proposed method effectively suppresses rain-induced distortions, improves DoA estimation accuracy, and enhances radar sensing performance in challenging weather conditions.