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.
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