The motivation of this work is to investigate the source number detectability by an oversampled or undersampled linear acoustic vector sensor (AVS) array. Sufficient conditions that ensure the extension of existing source number estimators and beamforming techniques from the acoustic pressure sensor (APS) array to the AVS array are established based on the linear independence of steering vectors of the AVS array, an issue closely related to the number of sources whose parameters [e.g., the direction of arrival (DOA)] can be uniquely determinable. The upper bound on the detectable number of sources with distinct DOAs under oversampled and undersampled array configurations is derived, where the difference of definitions of the distinct DOAs between the APS array and the AVS array is clarified. The number constraint on detectable sources with distinct spatial aliasing DOAs using the undersampled AVS array is also investigated. Under these conditions, advantages of eigenvalue-based source number estimators using AVS arrays are demonstrated, and the minimum description length (MDL) estimator is extended to the AVS array as an illustration to verify the established theories.

The post-process is widely adopted in passive sonar to improve the performance of target detection by raising the bearing resolution and suppressing the background noise, whereas the conventional algorithms separately adopted on the sub-bands always perform poorly when detecting weak target. To solve this problem, this paper proposes a method of filtering scanning azimuths for uniform line array based on the features of sub-band peaks. The proposed method enhances the sub-band peaks through the energy selection, modified Eckart filter, and noise weighting, wherein the scanning azimuths are filtered based on the accumulated energy and count features to finally preserve those near the target azimuth, which improves the normalized azimuth spectrum for better detection effect. This approach is validated by both simulated and measured data, and provides an idea for detecting the weak target submerged by the strong background interferences.

Source depth estimation is an important yet very difficult task for passive sonars, especially for horizontal linear arrays (HLAs). This paper proposes an efficient two-step depth estimation scheme using narrowband and broadband constructive and deconstructive striation patterns due to interference between the direct (D) and sea surface reflected (SR) arrivals at an HLA on the bottom of deep water. First, the horizontal source-array ranges are derived from triangulation results of solid angle estimates by subarray beamforming. The applicable areas of the method in deep water are investigated through Mento Carlo simulations, assuming different subarray partitioning ways of a given HLA aperture. Second, cost functions are built to match the measured beam intensity striations with modeled ones. To mitigate the spatial smoothing effect of the beam intensity striations during beamforming, a criterion of the largest subarray aperture is established, and a computationally efficient way is presented to model the replicas by the D-SR time delay templates at a single element of the array calculated by ray theory. The performance degradation due to limited source range spans, the distortion of the beam intensity striations, and range estimation errors has been analyzed. Two experimental datasets verify the effectiveness of the proposed method.

In shallow water, passive sonar usually has great difficulty in discriminating a surface acoustic source from an underwater one. To solve this problem, a supervised machine learning method using only one hydrophone is implemented in this paper. Firstly, simulated training data are generated by a normal mode model KRAKEN with the same environment setup as that in SACLANT 1993 experiment. Secondly, the k-nearest neighbor (kNN) classifiers are trained and evaluated using the scores of precision, recall, F1 and accuracy. Thirdly, the random subspace kNN classifiers are finely trained on three hyperparameters (the number of nearest neighbors, the number of predictors selected at random and the number of learners in the ensemble) to obtain the best model. Fourthly, a deep learning method called ResNet-18 is also applied, and it reaches the best balance between precision and recall, while the accuracies of both simulation and experimental data are all 1.0. Further, data from all 48 hydrophones of the vertical linear array (VLA) are analyzed using the three kinds of machine learning methods (kNN, random subspace kNN and ResNet-18) separately, and the results are compared. It is concluded that the performance of random subspace kNN is the best. Both the simulation and experimental results suggest the feasibility of machine learning as a surface and underwater acoustic source discrimination method even with only a single hydrophone.

This work investigates the problem of estimating directions of arrival (DOAs) of coherent sources without the left–right ambiguity by acoustic vector sensor linear arrays. Toeplitz matrices obtained from vector sensor cross covariance matrices are summed to obtain a new matrix with vector sensor directivities as weights. In this way, the proposed method restores the rank of the equivalent signal subspace and eliminates 2 π ambiguous DOA information of sources. A robust technique is also developed to improve its performance degradation because of the velocity sensor gain uncertainty during unit conversion, such as imprecise measurements of the sensor sensitivities and the product of the water density and sound speed in the water. The methods can be implemented as a preprocessing stage before applying beamformers, such as multiple signal classification and MVDR. Theoretical analyses show that comparing with existing preprocessing techniques, the proposed methods can fully resolve the coherent sources without the left–right ambiguity while keeping the effective array aperture, and even applicable to the case of closely spaced coherent sources. Their effectiveness and performances are conducted via simulations.