Abstract
Standard Direction of Arrival (DOA) estimation methods are typically
derived based on the Gaussian noise assumption, making them highly
sensitive to outliers. Therefore, in the presence of impulsive noise,
the performance of these methods may significantly deteriorate. In this
paper, we model impulsive noise as Gaussian noise mixed with sparse
outliers. By exploiting their statistical differences, we propose a
novel DOA estimation method based on sparse signal recovery (SSR).
Furthermore, to address the issue of grid mismatch, we utilize an
alternating optimization approach that relies on the estimated outlier
matrix and the on-grid DOA estimates to obtain the off-grid DOA
estimates. Simulation results demonstrate that the proposed method
exhibits robustness against large outliers.