Robust Covariance-Based DoA Estimation under Weather-Induced Distortion

TL;DR

This work tackles robust DoA estimation for ULAs under weather-induced distortions by adopting a physics-based $S$-matrix model that captures rain-induced phase and amplitude fluctuations. It leverages a Hermitian Toeplitz (HT) covariance structure and covariance matching through generalized least squares (GLS) to calibrate the distortion effects, followed by a phase-mensitive decoupling to recover the underlying array response. A key contribution is the decomposition of the combined covariance $\mathbf{R}_T = \mathbf{R}_x(\theta) \odot \mathbf{R}_b$ into a compact HT representation, enabling LS estimation of a small set of parameters and subsequent DoA via MUSIC. Numerical results demonstrate notable improvements in DoA accuracy and spectral sharpness under rain, validating the method’s effectiveness and potential applicability to other phase-noise environments.

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.

Figures (6)

• Figure 1: Phase difference pdf with different parameters
• Figure 2: Magnitude ratio pdf with different parameters
• Figure 3: Illustration of the array measurement model with the rain phase distortion.
• Figure 4: RMSE comparison between calibrated root-MUSIC-based DoA estimation and conventional root-MUSIC without calibration.
• Figure 5: Subdiagonal-wise comparison between the estimated and true distortion covariance matrices (results obtained at $\mathrm{SNR}=20\,\mathrm{dB}$).