Sparse spatial spectrum estimation for large aperture array under moving strong interferers

The performance of direction of arrival (DOA) estimation for targets using passive sonar arrays is significantly affected by strong interferers. Moreover, large-aperture arrays also encounter the snapshot-deficient problem when interferers move rapidly. To address these issues, a new sparse spatial spectrum estimation method is proposed. This method combines beam-space preprocessing to achieve interference suppression and reduce data dimensionality. During the preprocessing stage, adaptive nulls are initiated in the interferer direction and subsequently broadened with covariance matrix tapers. Sparse Bayesian learning is then applied to the preprocessed beam-space data to estimate the spatial spectrum. Compared to existing methods, beam-space preprocessing significantly reduces the impact of interferers in the stopband while enhancing the accuracy and computational efficiency of spatial spectrum estimation in the passband. Null broadening further strengthens robustness and avoids the snapshot-deficient problem. Simulation and experimental results demonstrate the capability of the proposed method to accurately estimate the multipath arrival angle of the target source even under moving strong interferers. Furthermore, the computational time required by the proposed approach is substantially lower than that of the element-space sparse method.