Sparsity-driven adaptive enhancement of underwater acoustic tonals for passive sonars
Yu Hao, Cheng Chi, Guolong Liang
Abstract
Acoustic tonals, radiated by underwater and surface vehicles, are an important feature for passive sonar detection. An adaptive line enhancer (ALE) is usually employed in passive sonar systems as a preprocessing step to enhance the acoustic tonals from these vehicles. Unfortunately, the performance of the conventional ALE is limited by the high steady-state misadjustment, which is caused by the weight noise in the adaptation process. This paper makes use of the frequency-domain sparsity of these tonals to develop better ALEs for passive sonars. The adaptation of the proposed ALE is performed in the frequency domain. Three typical sparse penalties, l1-norm, log-sum, and l0-pseudo-norm, are incorporated into the cost function of the frequency-domain adaptation, which yield three sparsity-driven ALEs: zero-attracting (ZA), reweighted zero-attracting (RZA), and l0. The simulation shows that the signal-to-noise ratio gains of the ZA-ALE, RZA-ALE, and l0-ALE are 5.9, 8.7, and 9.7 dB, higher than that of the conventional ALE, respectively. The results of processing the real data also validate that all the sparsity-driven ALEs outperform the conventional ALE, and the l0-ALE performs the best. The proposed sparsity-driven l0-ALE is thus a promising candidate for passive sonars to enhance the tonals.