Robust Deterministic DOA Estimation Using α-divergence in Unknown Noise Fields with Sparse Sensor Arrays
Wenjing Yang, Abd‐Krim Seghouane, Pavel Krupskii · 2025
In this paper, we address the problem of robust direction-of-arrival (DOA) estimation in unknown spatially cor-related noise fields using sensor arrays composed of subarrays in sparse configurations. In such arrays, the noise covariance matrix has a block-diagonal structure. The proposed robust DOA estimation method is derived from a parametric distribution divergence, α-divergence. Our approach can be viewed as an extension of existing Maximum Likelihood (ML) iterative procedures. The degree of robustness is controlled by the parameter α: as α→1, the proposed method converges to the traditional ML approach, while for α < 1, our method effectively mitigates the impact of potential outliers. Moreover, simulation studies show that our robust DOA estimation not only handles two types of outliers better than the ML method, but also exhibits high breakdown point properties.