Conventional beamforming (CBF) will lose its directivity once the signal frequency is much lower than the designed frequency of the array. In this paper, a modified differential beamforming (MDBF) method is proposed to deal with the underwater acoustic signal at low frequency band and then its application for direction of arrival (DOA) estimation with deconvolution technique is given. The MDBF proposed in this paper is a variation of the conventional differential beamforming (DBF) method and it constructs an optimization problem with an additional robustness constraint. The solution of MDBF solved via Lagrange multiplier technique owns a closed-form similar to that of DBF except two additional loading terms. Simulations with planar arrays show that for the frequency band where CBF owns an almost omnidirectional beampattern, MDBF outperforms DBF in robustness at the cost of some directivity loss. The application of MDBF for DOA estimation is demonstrated with both simulated and experimental data. The results show that the proposed MDBF outperforms both DBF and CBF in DOA estimation at low frequency band.

A method based on delay-and-sum (DAS) of cross-correlation functions is proposed for multipath time-delay estimation of a broadband signal received by a vertical line array (VLA) in the reliable acoustic path (RAP) environment. The multipath arrivals are mainly concerned with the direct (D) and surface-reflected (SR) paths. Three types of multipath time-delays are obtained, namely, the average D-D time-delay and SR-SR time-delay between two adjacent hydrophones, and the D-SR time-delay at the first hydrophone. The proposed method has significant advantages for underwater applications. First, the D and SR arrivals are successfully separated from time-delay estimation point of view, outperforming the estimation of direction of arrival with conventional beamforming. Second, the estimations of the average D-D time-delay and SR-SR time-delay between two adjacent hydrophones occur at the diagonal line of the two-dimensional DAS outputs, facilitating the automatic detection and extraction of multipath time-delays. Third, the estimation is robust against noisy background noise due to the spatial accumulative effect. The method is applied to experimental data for explosive source at ~17km range and 300m depth, where it is shown that simple, stable, and fairly accurate multipath time-delay estimation can be made.

Reverberation is the main background interference for active sonar detection in shallow sea. Reverberation suppression is crucial for enhancing the performance of active sonar. In this paper, a reverberation suppression method based on low-rank sparse decomposition is proposed. First, both the sparseness property of the targets and the non-local self-correlation property of the reverberation are used to construct a range azimuth patch matrix model. The reverberation suppression problem is then transformed into an optimization problem for the recovery of a low-rank sparse matrix. The validity of the proposed method is verified by using the measured data. Results show that, compared with the reverberation pre-whitening and sparse fractional Fourier transform methods, the proposed method significantly improves the reverberation suppression performance and achieves a better detection result when the signal-to-interference ratio is below −2 dB.

Accurate underwater target recognition is challenged by high noise levels, signal attenuation, multipath reflections, and the need for efficient real-time processing in complex environments. To overcome these obstacles, we propose the lightweight time-frequency-space dual-stream network (LTFSD-Net), which is a novel framework designed to enhance active sonar target recognition by effectively extracting and fusing spatial-, temporal-, and frequency-domain features. Our dual-stream architecture incorporates a beamforming-based spatial attention mechanism and a depth attention mechanism, enabling the network to focus on critical regions of target echoes and improve feature extraction. In addition, lightweight depthwise separable convolutional modules facilitate efficient time-frequency feature extraction, ensuring computational efficiency that is suitable for real-time applications. We introduce a novel feature fusion strategy that employs tensor decomposition at the front-end and compact bilinear pooling at the back-end. This approach seamlessly combines low- and high-level features, thereby enhancing the discriminative capabilities of the model. Extensive experiments conducted on both sea trial and anechoic pool datasets demonstrated that LTFSD-Net achieved overall accuracies of 84.36% and 92.88%, respectively. The proposed method outperformed existing state-of-the-art approaches in terms of both accuracy and computational efficiency, underscoring its effectiveness and efficiency. Our findings highlight its potential for integration into advanced sensor systems for enhanced underwater acoustic target recognition.