Underwater acoustic target signal enhancement algorithm optimized by feature preservation and noise update

The enhancement effect of the classic Nonnegative Matrix Factorization (NMF) applied to underwater acoustic target signal is unsatisfactory for the feature overlap of underwater acoustic target signal and the variability of ocean underwater acoustic field. And so, an improved NMF underwater acoustic signal enhancement algorithm based on feature preservation is proposed by adaptively improving the classic NMF algorithm using the characteristics of underwater acoustic target signals. The β-divergence constraints and similarity detection is first applied to the NMF feature basis matrix to eliminate redundancy, optimized NMF features with size invariant characteristics, while avoiding the loss of basis vectors due to coefficient dispersion caused by similar basis vectors. And then, the real-time environmental noise received by the sonar is used to improve the adaptability of the NMF noise basis vector, achieving noise reduction and enhancement of the underwater acoustic target signal. The experimental results show that, compared to the classical NMF, the manifold constrained NMF that is used to the underwater acoustic signal enhancement, the proposed method achieved the better signal enhancement effect.