The detection performance of active sonar is often hindered by the presence of seabed reverberation in shallow water. Separating the reverberations from the target echo and noise in the received signal is a crucial challenge in the field of underwater acoustic signal processing. To address this issue, an improved Go-SOR decomposition method is proposed based on the subspace-orbit-randomized singular value decomposition (SOR-SVD). This method successfully extracts the low-rank structure with a certain striation pattern. The results demonstrate that the proposed algorithm outperforms both the original Go algorithm and the current state-of-the-art (SOTA) algorithm in terms of the definition index of the low-rank structure and computational efficiency. Based on the monostatic reverberation theory of the normal mode, it is established that the low-rank structure is consistent with the low-frequency reverberation interference striation. This study examines the interference characteristics of the low-rank structure in the experimental sea area and suggests that the interferences of the fifth and seventh modes mainly control the low-rank structure. The findings of this study can be applied to seafloor exploration, reverberation waveguide invariant (RWI) extraction, and data-driven reverberation suppression methods.

The performance of active sonar detection systems is seriously affected by the reverberation at the bottom of the waveguide in shallow water. In order to improve the performance of active sonar detection, it is necessary to understand the horizontal correlation of shallow-water bottom reverberation in active towed-array processing technology. However, the current research on the spatial correlation of reverberation is mainly based on vertical correlation, little work has been done on the horizontal correlation characteristics of long-distance seabed reverberation, and there is no support from sea test data. In this paper, the coupled mode reverberation model is applied to the horizontal correlation, and is studied according to the receiving position, time, and frequency. The simulation results show that, for the long-range bottom reverberation, the lateral correlation is greater than the longitudinal correlation in the horizontal space. By introducing the adiabatic mode solution, the mathematical model of horizontal correlation in the range-dependent waveguide with depth is derived. The numerical results show that the influence of the seabed dip angle on the horizontal correlation should be considered and that the horizontal correlation is affected obviously by the propagation effects of the sloped sea floor. Finally, the experimental data processing and analysis are given and verify the correctness of the algorithm.

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.

In this paper, to counteract the sensitivity of the traditional Hough transform to noise and the fluctuations in parameter estimation, we propose a hyperbolic warping transform that integrates all interference fringes in the time–frequency domain to accurately estimate the motion parameters of a single hydrophone. This method can accurately estimate the target motion parameters, including the time of closest point of approach ($t_{CPA}$), the ratio of the nearest distance to the speed ($b=r_{CPA}/v$), and the waveguide invariant ($\beta$). The two algorithms are compared by simulation and sea trial experiments. Hyperbola-warping improves the noise immunity performance by 10 dB in simulation experiments, increases the detection range by 20% in sea trial experiments, and demonstrates that the method proposed in this paper has better noise resistance and practicality.

This letter investigates the asymptotic spectral distribution (ASD) of a stochastic second-order progressive scattering channel (PSC), a prevalent component in radar and communication systems. Leveraging random matrix theory (RMT), we derive a precise probability density function (PDF) consist of basic algebraic structure. The accuracy of the proposed ASD is validated through rigorous Monte Carlo experiments, demonstrating a strong alignment between the theoretical distribution and its numerical representation. Subsequently, utilizing the proposed ASD, we compute the channel capacity parameters of the secondorder PSC, encompassing the normalized input-output mutual information (IOMI) and the arithmetic mean over minimum mean square error (MMSE). Our results indicate superior IOMI performance and confirm that the channel capacity of the secondorder PSC is markedly lower than that of the TRC. Furthermore, we anticipate the potential application of the ASD to acoustic scattering and reverberation problems at the sea surface and seabed.

In the context of active sonar detection, ocean reverberation, as the main clutter in active sonar detection, carries scattering characteristic information of non-uniform media. At the same time, the chaotic ocean reverberation signal causes serious background interference to target detection, and has been widely studied and researched by scholars at home and abroad. In order to cope with the performance analysis and indicator demonstration of active sonar detection, it is often necessary to predict and analyze reverberation. The modeling theory of ocean reverberation in the form of pulse signals is relatively mature. In response to the long pulse width of continuous signals and the failure of the single scattering assumption theory, this paper establishes a unified bottom reverberation model for shallow and deep sea environments using the theory of normal waves and ray. The random segmented superposition algorithm is introduced to simulate seabed reverberation in the form of continuous signals. The influence of different segmentation methods on reverberation intensity is discussed, and the reliability of the simulation results is confirmed by combining with sea trial experimental data.