Direction of arrival (DOA) estimation via sensor array is a crucial component of any passive sonar signal processing technology. In certain practical applications, however, the interested far-field targets are frequently affected by near-field interference, which may result in degradation of DOA estimation. Aiming at the direction estimation problems of far-field targets under strong near-field interference, a unified sparse representation model of far-field and near-field hybrid sources is constructed according to the various correlations in steering vectors between the planar wave and spherical wave in this paper. A high-resolution spatial spectrum reconstruction algorithm based on a sparse Bayesian framework is then exploited to constrain the energy of near-field interference in the preset near-field steering vector over-complete dictionary, thus ensuring the accurate detection and estimation of far-field targets. An expectation-maximization (EM) algorithm approach is introduced to estimate the number of sources and noise power iteratively, which will reduce the dependence of the algorithm on such prior information. Several state-of-art algorithms are mentioned and discussed (Minimum Variance Distortionless Response (MVDR) method, Multiple Signal Classification (MUSIC) algorithm and conventional beamforming (CBF) algorithm) to compare with the one proposed in this manuscript that achieves higher accuracy of estimation and resolution under low SNR level with limited samples, which is verified by simulation and for the results obtained in an experimental case study.

Direction-of-arrival (DOA) estimation in a spatially isotropic white noise background has been widely researched for decades. However, in practice, such as underwater acoustic ambient noise in shallow water, the ambient noise can be spatially colored, which may severely degrade the performance of DOA estimation. To solve this problem, this paper proposes a DOA estimation method based on sparse Bayesian learning with the modified noise model using acoustic vector hydrophone arrays. Firstly, an applicable linear noise model is established by using the prolate spheroidal wave functions (PSWFs) to characterize spatially colored noise and exploiting the excellent performance of the PSWFs in extrapolating band-limited signals to the space domain. Then, using the proposed noise model, an iterative method for sparse spectrum reconstruction is developed under a sparse Bayesian learning (SBL) framework to fit the actual noise field received by the acoustic vector hydrophone array. Finally, a DOA estimation algorithm under the modified noise model is also presented, which has a superior performance under spatially colored noise. Numerical results validate the effectiveness of the proposed method.

The complex acoustic intensity method is one of the common methods used for the azimuth estimation of single-vector sensors. However, this method establishes a relationship between frequency and azimuth, which limits its practical applicability for multiple linear frequency modulation (LFM) signals with overlapping frequency domains. In this paper, the time–frequency distribution information of the LFM signal is combined with the complex acoustic intensity method, and more signal parameter information is used to expand the application scenario of the single-vector sensor. The proposed method first processes the time–frequency graph of the signal to obtain a stable and clear time–frequency distribution, and then obtains the acoustic intensity distribution of the signal using the time integration of the energy on the ridge of the signal to estimate the target orientation more stably. The simulation results show that the root mean square error of azimuth estimation is less than 1° when the SNR is greater than 0 dB. Furthermore, a pool experiment was carried out to verify the effectiveness of the proposed method.

Detecting weak far-field signals in underwater environments via oceanic engineering is extremely important. Recently, underwater acoustic beacon signal detection has become a popular topic. In this study, the time-frequency analysis of short-time Fourier transform is studied for periodic pulse signal detection, and a long-term multiperiod coherent integration method with a data group alignment technique is proposed. The simulation analyzes the detection performance and delay estimation accuracy of the proposed method. When the integration time or signal-to-noise ratio is limited, the proposed method demonstrates better performance, which is beneficial for practical applications.

Recently, the direction of arrival estimation with co-prime arrays has gradually been applied in underwater scenarios because of its significant advantages over traditional uniform linear arrays. Despite the advantages of co-prime arrays, the spatial spectra obtained directly from conventional beamforming can be degraded by grating lobes due to the sparse spatial sampling in passive sensing applications, which will seriously deteriorate the estimation performance. In this paper, capon beamforming is applied to a co-prime sensor array as a pretreatment before high-resolution direction of arrival (DOA) estimation methods. The amplitudes extracted from the beam-domain outputs of two subarrays and the phases extracted from the cross-spectrum of the spatial spectrum are exploited to suppress the spurious peaks in beam patterns and eliminate ambiguities. Consequently, interference can be further mitigated, and the performance of high-resolution DOA methods will be guaranteed. Simulations show that the method proposed can improve the reliability and accuracy of DOA estimation with great value in practice.