Direction of arrival (DOA) estimation using coprime arrays is a significant topic in array signal processing. Existing approaches typically assume either Gaussian or impulsive noise, limiting their effectiveness under mixed conditions. This paper proposes a DOA estimation method for coprime arrays in the presence of mixed noise composed of symmetric \(\alpha \) -stable (S \(\alpha \) S) impulsive and nonuniform Gaussian noise. A condition selection phase fractional lower-order moment (PFLOM) replaces the conventional second-order covariance, addressing the convergence failure caused by impulsive noise of varying intensity. To further mitigate nonuniform noise while preserving signal components, a covariance matrix reconstruction is introduced. Based on this, a Toeplitz-structured virtual covariance matrix is constructed via baseline mapping, enabling effective DOA estimation. Simulations show the proposed method achieves higher accuracy and robustness than existing algorithms, particularly under low generalized signal-to-noise ratio in mixed noise.
