Sparsity‐based adaptive line enhancer for passive sonars

Abstract

Adaptive line enhancers (ALEs) have been widely used in passive sonars for enhancing narrowband discrete components (known simply as lines or tonals) radiated by surface or underwater targets. Unfortunately, the performance of the conventional ALE based on the least-mean-square (LMS) adaptive algorithm is limited by the high steady-state misadjustment, which limits the output signal-to-noise ratio (SNR) of the conventional ALE. To break through the limit of the conventional ALE, this study proposes a sparsity-based ALE (SALE). The SALE takes into account the frequency-domain sparsity of narrowband discrete components and realises the adaption in the frequency domain. A l 1 -norm sparse penalty is incorporated into authors’ frequency-domain adaption to reduce the misadjustment dramatically. The simulations show that the SNR gain of the SALE is 6.3 dB higher than that of the conventional ALE. The results of processing the real data collected in the sea trial also demonstrate that authors’ proposed SALE outperforms the conventional ALE. To reduce the computational complexity of the SALE, a fast SALE (FSALE) is also proposed. In authors’ setup, the computational complexity of the FSALE is one order of magnitude lower than that of the SALE, with comparable SNR performance.