A passive broadband source localization approach in the deep ocean is proposed based on the coherent matched-field processing combined with a Riemannian distance. The spatial coherence between hydrophones over frequency bandwidth is utilized for eliminating the unknown source spectrum. Three Riemannian distances are introduced for the measurement of the similarity between the data and replica matrices. The experimental results indicate that the proposed processors with a short array can effectively suppress the sidelobe and improve the localization performance. The source localization influenced by the factors such as the aperture, number of hydrophones, and desired bandwidth is also demonstrated.

The movement of sonar platform and the time-varying underwater acoustic environment introduce additional unstable reverberation component, resulting in a degradation of the performance of traditional methods that exploit the low-rank characteristics of multi-frame reverberation backgrounds for reverberation suppression. In order to solve this problem, a reverberation suppression method for moving sonar platform based on tensor decomposition is proposed. In this method, the multi-frame data received on a moving sonar platform containing both target echoes and background reverberation is modeled as the superposition of spatiotemporal continuous reverberation, random reverberation and sparse target in the form of a tensor. A cost function is formed in which anisotropic three-dimensional (range, bearing, time) total variation and non-convex rank approximation constrain continuous reverberation, the Frobenius norm constrains random reverberation, and a tensor-weighted Lp norm constrains sparse target. The constrained optimization problem is solved using the alternating direction multiplier method (ADMM), enhanceing computational efficiency. The performance of this method is evaluated on a dataset containing a moving target and reverberation that changes rapidly in time due to the motion of the sonar platform. Experimental results show that compared with several traditional methods, the proposed method is more robust to rapidly evolving reverberation caused by the motion of the sonar platform and can retain the target energy to a greater extent.

Wind-driven and distant shipping noise sources contribute to the total noise field in the deep ocean direct-arrival zones. Wind-driven and distant shipping noise sources may significantly and simultaneously affect the spatial characteristics of the total noise field to some extent. In this work, a ray approach and parabolic equation solution method were jointly utilized to model the low-frequency ambient noise field in a range-dependent deep ocean environment by considering their calculation accuracy and efficiency in near-field wind-driven and far-field distant shipping noise fields. The reanalysis databases of National Center of Environment Prediction (NCEP) and Volunteer Observation System (VOS) were used to model the ambient noise source intensity and distribution. Spatial vertical directionality and correlation were analyzed in three scenarios that correspond to three wind speed conditions. The noise field was dominated by distant shipping noise sources when the wind speed was less than 3 m/s, and then the spatial vertical directionality and vertical correlation of the total noise field were nearly consistent with those of distant shipping noise field. The total noise field was completely dominated by near field wind generated noise sources when the wind speed was greater than 12 m/s at 150 Hz, and then the spatial vertical correlation coefficient and directionality pattern of the total noise field was approximately consistent with that of the wind-driven noise field. The spatial characteristics of the total noise field for wind speeds between 3 m/s and 12 m/s were the weighted results of wind-driven and distant shipping noise fields. Furthermore, the spatial characteristics of low-frequency ambient noise field were compared with the classical Cron/Sherman deep water noise field coherence function. Simulation results with the described modeling method showed good agreement with the experimental measurement results based on the vertical line array deployed near the bottom in deep ocean direct-arrival zones.

Matched-field processing (MFP) achieves underwater source localization by measuring the correlation between the array and replica signals, with traditional MFP being equivalent to estimating the Euclidean distance between the data cross-spectral density matrix (CSDM) and replica matrices. However, in practical applications, random inhomogeneities in the marine environment and inaccurate estimation of CSDM reduce MFP performance. The traditional minimum variance matched-field processor with environmental perturbation constraints perturbs a priori environment parameters to obtain linear constraints and yields the optimal weight vectors as the replica vectors. In this paper, within the framework of information geometry, the geometric properties of CSDMs as semi-positive definite and Hermitian enable CSDMs to be described as points in a Riemannian manifold. Source localization can be achieved by quantifying the similarity between the CSDMs as the geodesic distance between the points on the manifold. This paper introduces a constrained replica CSDM composed of perturbed replica vectors and proposes a robust matched-field processor based on two non-Euclidean distances: the Riemannian distance and the modified Jensen–Shannon distance. Simulations and experimental results demonstrate that the proposed processors are more robust against environmental and statistical mismatches than traditional processors and can also reduce sidelobe level and improve the resolution.

Passive multiple sound source localization is a challenging problem in underwater acoustics, especially for a short hydrophone array in the deep ocean. Several attempts have been made to solve this problem by applying compressive sensing (CS) techniques. In this study, one greedy algorithm in CS theory combined with a spatial filter was developed and applied to a two-source localization scenario in the deep ocean. This method facilitates localization by utilizing the greedy algorithm with a spatial filter at several iterative loops. The simulated and experimental data suggest that the proposed method provides a certain localization performance improvement over the use of the Bartlett processor and the greedy algorithm without a spatial filter. Additionally, the effects on the source localization caused by factors such as the array aperture, number of hydrophones or snapshots, and signal-to-noise ratio (SNR) are demonstrated.

Matched field processing (MFP), a technique that is extensively used for underwater passive source ranging, primarily faces a challenge of replica environmental mismatch. In this paper, a correction physics-informed neural network (CrPINN)-aided MFP (CrPIMFP), a data-efficient and physics-conforming ranging method, is proposed. The CrPINN uses very few measured data to correct replicas generated by acoustic propagation model to mitigate mismatches. During this process, a normalized correction loss is applied to align the replica with the measured field. Additionally, CrPINN integrates the Helmholtz equation and the boundary condition into loss function, thereby enabling interpolation to unsampled points of the replica and decreasing the dependence on labeled data. The discrepancy between the corrected replica generated by the trained CrPINN and the measured field is reduced, which is beneficial for the ranging performance of MFP. Simulation results demonstrate that, compared with MFP, CrPIMFP is more resistant to bottom sediment depth/sound speed mismatches and mildly range-dependent environment in shallow water. The experimental results from the SWellEx-96 environment show that CrPIMFP outperforms some conventional ranging algorithms in four scenarios—few training samples with varying sampling conditions, generalization to unseen environments, few array elements, and great distances—verifying its robustness.