Least Mean p-Power-Based Sparsity-Driven Adaptive Line Enhancer for Passive Sonars Amid Under-Ice Noise

Abstract

In order to detect weak underwater tonals, adaptive line enhancers (ALEs) have been widely applied in passive sonars. Unfortunately, conventional ALEs cannot perform well amid impulse noise generated by ice cracking, snapping shrimp or other factors. This kind of noise has a different noise model compared to Gaussian noise and leads to noise model mismatch problems in conventional ALEs. To mitigate the performance degradation of conventional ALEs in under-ice impulse noise, in this study, a modified ALE is proposed for passive sonars. The proposed ALE is based on the least mean p-power (LMP) error criterion and the prior information of the frequency domain sparsity to improve the enhancement performance under impulse noise. The signal-to-noise ratio (SNR) gain is chosen as the metric for evaluating the proposed ALE. The simulation results show that the output SNR gain of the proposed ALE was, respectively, 9.3 and 2.6 dB higher than that of the sparsity-based ALE (SALE) and the least mean p-power ALE (PALE) when the input GSNR was −12 dB. The results of processing the under-ice noise data also demonstrate that the proposed ALE is distinguished among the four ALEs.