Robust Direction-of-Arrival Estimation in the Presence of Outliers

In this article, we propose a robust technique for direction-of-arrival (DOA) estimation in the presence of outliers, which may be caused by either impulsive noise or malfunctioning sensors in the array. Conventional DOA estimation algorithms like multiple signal classification (MUSIC) cannot accurately estimate the DOAs from outlier-corrupted data because the array covariance matrix is poorly estimated. We propose a penalized likelihood approach to robustly estimate the signal (low-rank) part of the array covariance matrix and apply MUSIC to this estimated covariance matrix to find the DOAs. The penalty in the proposed algorithm is derived from the principle of false discovery rate. Simulation results verify the effectiveness of the proposed approach when compared with state-of-the-art robust DOA estimation algorithms.