In shallow water, the reverberation interference striations (RISs) are often polluted by serious random seafloor scattering, and it is the main limitation for the performance of active sonar. In this letter, a conditional generative adversarial network (CGAN) is used to recover the clear RISs from distorted ones. The experimental distorted RIS data induced by an explosive source are imported into the well-trained CGAN, and the precise interference striations are obtained. This means that the deterministic part of reverberation can be extracted exactly from the stochastic scattering field. The CGAN is robust when reverberation-to-noise ratios are higher than 2 dB.

No abstract

No abstract

Bottom reverberation is an important limitation that affects the detection performance of active sonar in shallow water. The stochastic rough seafloor surface scattering is one of the main scattering sources of the bottom reverberation. Since there are much uncertainties, such as propagation fluctuation, unstable station of transmit and receive equipment, it is difficult to investigate the quantitative relationship between the random rough interface parameters and the statistical property by processing the experimental reverberation data collected in the ocean In this paper, the statistical characteristics of the irregular 3D printed interface scattering field are studied by a scaled tank experiment, and the detailed results are presented by the comparison between the COMSOL simulations and the recorded data, the feasibility of the random rough interfaces experiment is verified well. Because the relationship between parameters is precisely controllable, it lays a foundation for the subsequent experimental research of target extraction under the background of interface scattering.

No abstract