Reverberation suppression of underwater moving sonar platform based on tensor decomposition

The movement of sonar platform and the time-varying underwater acoustic environment introduce additional unstable reverberation component, resulting in a degradation of the performance of traditional methods that exploit the low-rank characteristics of multi-frame reverberation backgrounds for reverberation suppression. In order to solve this problem, a reverberation suppression method for moving sonar platform based on tensor decomposition is proposed. In this method, the multi-frame data received on a moving sonar platform containing both target echoes and background reverberation is modeled as the superposition of spatiotemporal continuous reverberation, random reverberation and sparse target in the form of a tensor. A cost function is formed in which anisotropic three-dimensional (range, bearing, time) total variation and non-convex rank approximation constrain continuous reverberation, the Frobenius norm constrains random reverberation, and a tensor-weighted Lp norm constrains sparse target. The constrained optimization problem is solved using the alternating direction multiplier method (ADMM), enhanceing computational efficiency. The performance of this method is evaluated on a dataset containing a moving target and reverberation that changes rapidly in time due to the motion of the sonar platform. Experimental results show that compared with several traditional methods, the proposed method is more robust to rapidly evolving reverberation caused by the motion of the sonar platform and can retain the target energy to a greater extent.