An Underwater Target Location Algorithm Combined with Schmidt Orthogonalization

Abstract In underwater target localization research, where challenges such as low signal-to-noise ratio and multiple interferences exist in conventional matched field processing, this paper presents a novel approach combining compressed sensing with Schmidt orthogonalization. Initially, an orthogonalization procedure is applied to the observation matrix, yielding a new matrix with reduced column coherence and maximal adherence to the Restricted Isometry Property (RIP) constraint. Subsequently, the Sparse Adaptive Matching Pursuit (SAMP) algorithm from compressed sensing is employed to reconstruct the signal, leading to precise underwater target localization. Simulation experiments substantiate that this combined approach outperforms the conventional matched field algorithm by yielding a higher signal-to-noise ratio for target acoustic source localization and markedly reducing the count of interference virtual sources. Consequently, the performance of localization for targets is substantially enhanced.