Array error calibration and direction of arrival estimation based on joint sparse recovery

In practical sonar systems and complex underwater environments, problems such as sensor position errors, system frequency errors, and acoustic propagation errors exist, which seriously affect the estimation accuracy of the direction of arrival (DOA) of targets. To solve this problem, this paper proposes an array error calibration and DOA estimation method based on joint sparse recovery. First, a signal model incorporating multi-snapshot fused array errors is constructed, which makes full use of the information of each snapshot and considers the phase error caused by array errors, thus improving the stability and robustness of the algorithm. Then, a global metric-based optimization problem for DOA estimation is formulated to minimize the influence of phase errors, which achieves both DOA sparse recovery and phase error calibration simultaneously. Finally, a low-complexity dual-iterative optimization algorithm is proposed. In each iteration, the model is calibrated using the phase error estimated in the previous iteration, enabling fast convergence of DOA estimation. Simulation experiments and real-data experiments show that the proposed method can still achieve high-precision and low-sidelobe DOA estimation results in the presence of random phase errors and strong noise.