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

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 has a different model compared Gaussian and leads mismatch problems ALEs. To mitigate the performance degradation under-ice noise, this study, modified ALE is proposed for The based on least mean p-power (LMP) error criterion prior information frequency domain sparsity improve enhancement under noise. signal-to-noise ratio (SNR) gain chosen as metric evaluating ALE. simulation results show that output SNR was, respectively, 9.3 2.6 dB higher than sparsity-based (SALE) (PALE) when input GSNR was −12 dB. processing data also demonstrate distinguished among four