Sparsity-inducing frequency-domain adaptive line enhancer for unmanned underwater vehicle sonar

Abstract The radiated lines from underwater targets are a significant feature for passive sonar detection. Since the spatial and time processing gains are limited in an unmanned underwater vehicle (UUV) sonar, an adaptive line enhancer (ALE) is usually employed as a preprocessing step to enhance the signal-to-noise ratio (SNR) of the lines. However, the original time-domain ALE based on the least mean square (LMS) algorithm suffers from the weight noise in the adaptation, which limits the SNR gain severely. Inspired by the frequency-domain sparsity of the lines, a sparsity-inducing ALE is developed to break through this limitation. The proposed ALE is implemented in the frequency domain and a reweighted L1-norm sparse penalty is incorporated into the cost function of the adaptation. By means of the sparse penalty, a zero-attraction term is added in the adaptation, which results in the suppression of the weight noise. The SNR gain is thus enhanced. In the proposed ALE, the sliding discrete Fourier transform (SDFT) technique is utilized to transfer the time-domain input samples to the frequency domain, and the computational complexity of the proposed ALE is thus comparable to that of the original one, which meets the energy efficiency requirement of UUV. Simulation results demonstrate that the SNR gain of the proposed ALE is over 9 dB higher than that of the original ALE with an input SNR of −25 dB. Experimental data processing also verifies the superiority of the proposed ALE.