Null Broadening Beamforming for Passive Sonar Based on Weighted Similarity Vector

Abstract

Beamforming technology is very important for passive sonar to detect targets. However, the performance of beamformer is seriously degraded in practical applications due to the complex and changeable underwater environment. In this paper, a null broadening algorithm for passive sonar based on weighted similarity vector is proposed for underwater fast moving strong interference signals. First, The covariance matrix is reconstructed through the correlation between the steering vector and the subspace eigenvector, which is used to calculate the similarity vector. Then, the maximum power in the interference angle sector is used as the virtual interference source power to broaden the null trap in the angle sector. Next, the difference between the optimal weight vector and the similar vector is minimized, interference-plus-noise power constraints and norm constraints are added, and it is written as a quadratic constrained quadratic programming (QCQP) problem, which is converted into a convex optimization problem by using the semidefinite relaxation technique. Finally, the optimal solution is calculated by using eigen decomposition. The simulation results show that the algorithm can guarantee deep nulling and effectively suppress sidelobe height under various error conditions, which shows that the proposed algorithm has good suppression effect and strong robustness for fast strong interference.