When a feed-forward neural network (FNN) is trained for acoustic source ranging in an ocean waveguide, it is difficult evaluating the FNN ranging accuracy of unlabeled test data. The label is the distance between source and receiver array. A fitting-based early stopping (FEAST) method is introduced to evaluate the FNN ranging error on test data where the distance to the source is unknown. Based on FEAST, when the evaluated ranging error is minimum on test data, training is stopped. This will improve the FNN ranging accuracy on the test data. The FEAST is demonstrated on simulated and experimental data.

A U-Net is trained to recover acoustic interference striations (AISs) from distorted ones. A random mode-coupling matrix model is introduced to generate a large number of training data quickly, which are used to train the U-Net. The performance of AIS recovery of the U-Net is tested in range-dependent waveguides with nonlinear internal waves (NLIWs). Although the random mode-coupling matrix model is not an accurate physical model, the test results show that the U-Net successfully recovers AISs under different signal-to-noise ratios and different amplitudes and widths of NLIWs for different shapes.

In the field of target localization, improving direction-of-arrival (DOA) estimation methods for weak targets in the context of strong interference remains a significant challenge. This paper presents a robust DOA estimator for localizing weak signals of interest in an environment with strong interfering sources that improve passive sonar DOA estimation. The presented estimator combines a multiple-measurement-vector orthogonal matching pursuit (MOMP) algorithm and a dimension-reduced matrix filter with deep nulling (DR-MFDN). Strong interfering sources are adaptively suppressed by employing the DR-MFDN, and the beam-space passband robustness is improved. In addition, Gaussian pre-whitening is used to prevent noise colorization. Simulations and experimental results demonstrate that the presented estimator outperforms a conventional estimator based on a dimension-reduced matrix filter with nulling (DR-MFN) and the multiple signal classification algorithm in terms of interference suppression and localization accuracy. Moreover, the presented estimator can effectively handle short snapshots, and it exhibits superior resolution by considering the signal sparsity.

Monochromatic sound source localization becomes difficult in enclosed space. According to the reciprocity theorem, a self-consistent method of source localization in enclosed space, referred to as the flux projection beamforming, is proposed, only using the measurement of the sound pressure and normal velocity on the closed boundary at a single frequency. Its validity is examined both by experiment and simulation.

A multi-resolution estimation method for interference spectrum separation from a one-dimensional broadband acoustic intensity is presented in this letter. The key of the proposed method is the recursion reconstruction and singular value decomposition of a 2×L Hankel matrix, which is constructed by a broadband intensity data sequence of length L + 1. Numerical simulation results show that this method can be used to achieve the zero-phase-shift estimation of each interference spectrum without any prior environmental information.

The passive estimation of harmonic sound source depth is of great significance for underwater target localization and identification. Passive source depth estimation using a single hydrophone with an unknown seabed geoacoustic property is a crucial challenge. To address this issue, a harmonic sound source depth estimation algorithm, seabed independent depth estimation (SIDE) algorithm, is proposed. This algorithm combines the estimated mode depth functions, modal amplitudes, and the sign of each modal to estimate the sound source depth. The performance of the SIDE algorithm is analyzed by simulations. Results show that the SIDE is insensitive to the initial range of the sound source, the source depth, the hydrophone depth, the source velocity, and the type of the seabed. Finally, the effectiveness of the SIDE algorithm is verified by the SWellEX-96 data.