This paper aims to detect a target that crosses the baseline connecting the source and the receiver in shallow-water environments, which is a special scenario for a bistatic sonar system. In such a detection scenario, an intense sound wave, known as the direct blast, propagates directly from the source to the receiver without target scattering. This direct blast usually arrives at the receiver simultaneously with the forward scattering signal and exhibits a larger intensity than the signal, posing a significant challenge for target detection. In this paper, a range-domain subspace is constructed by the horizontal distance between the source/target and each element of a horizontal linear array (HLA) when the ranges of environmental parameters are known a priori. Meanwhile, a range-domain subspace detector based on direct blast suppression (RSD-DS) is proposed for forward scattering detection. The source and the target are located at different positions, so the direct blast and the scattered signal are in different range-domain subspaces. By projecting the received data onto the orthogonal complement subspace of the direct blast subspace, the direct blast can be suppressed and the signal that lies outside the direct blast subspace is used for target detection. The simulation results indicate that the proposed RSD-DS exhibits a performance close to the generalized likelihood ratio detector (GLRD) while requiring less prior knowledge of environments (only known are the ranges of the sediment sound speed and the bottom sound speed), and its robustness to environmental uncertainties is better than that of the latter. Moreover, the proposed RSD-DS exhibits better immunity against the direct blast than the GLRD, since it can still work effectively at a signal-to-direct blast ratio (SDR) of −30 dB, while the GLRD stops working in this case.

Underwater radiated noise power estimation is crucial for the quantitative assessment of noise levels emitted by ships and underwater vehicles. This paper therefore proposes a maximum likelihood estimation method for determining the power of underwater radiated noise. The method establishes the probability density function of the hydrophones array received data and derives the minimum variance unbiased estimation of the power through theoretical analysis under the maximum likelihood criterion. Numerical simulations and experimental data demonstrate that this method can significantly reduce the influence of ambient noise on estimation results and improve the estimation accuracy under low signal-to-noise ratio conditions, outperforming commonly used beamforming-based estimation methods. In addition, the estimation variance achieves the Cramér–Rao lower bound, which is consistent with theoretical derivation. When the source position is unknown, this method can simultaneously localize the sound source and estimate its power by searching for the maximum value within a specified region.

Presented in this study is a novel method for estimating the depth of single underwater source in shallow water, utilizing vector sensors. The approach leverages the depth distribution of the broadband Stokes parameters to estimate source depth accurately. Unlike traditional matched field processing (MFP) and matched mode processing (MMP), the proposed approach can estimate source depth directly from the data received by sensors without requiring complete environmental information. Firstly, the broadband Stokes parameters (BSP) are established using the normal mode theory. Then the nonstationary phase approximation is used to simplify the theoretical derivation, which is necessary when dealing with broadband integrals. Additionally, range terms of the BSP are eliminated by normalization. By analyzing the depth distribution of the normalized broadband Stokes parameters (NBSP), it is found that the NBSP exhibit extreme values at the source depth, which can be used for source depth estimation. So the proposed depth estimation method is based on searching the peaks of the NBSP. Simulations show that this method is effective in relatively simple shallow water environments. Finally, the effect of source range, frequency bandwidth, sound speed profile (SSP), water depth and Signal-to-Noise Ratio (SNR) are studied. The findings indicate that the proposed method can accurately estimate the source depth when the SNR is greater than -5dB and does not need to consider model mismatch issues. Additionally, variations in environmental parameters have minimal impact on estimation accuracy. Compared to MFP, the proposed method requires a higher SNR, but demonstrates superior robustness against fluctuations in environmental parameters.