Sparse spatial spectral estimation with heavy sea bottom reverberation in the fractional fourier domain

In shallow water, the target bearing estimation performance of active detection methods can be degraded, when the underwater scene is complicated by the presence of heavy sea bottom reverberation. To address this problem, this paper proposes an improved approach. Based on the principle of fractional Fourier transform (FrFT) on a linear frequency modulation signal, reverberation model for active detection in shallow water is established in the fractional Fourier (FrF) domain. Based on the characteristics of high peak-signal-to-reverberation ratio (PSRR), an approach of sparse spatial spectral estimation based on singular value decomposition is proposed. To verify the performances of reverberation suppression and high resolution of the proposed approach, target bearing estimation simulations are conducted with simulated heavy sea bottom reverberation, and the effect of the signal-to-noise ratio (SNR) on the detection probability of the proposed method is provided. An experiment was conducted in a harbor with heavy sea bottom reverberation, and the proposed approach was applied to the experimental data. Compared with conventional methods, the proposed method not only achieves the target bearing with high resolution, but also suppresses the reverberation considerably. Furthermore, the average value of the spatial spectrum of the sea bottom reverberation is reduced by a factor of 5.3.