Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

Recent advancements in machine learning (ML) techniques applied to underwater acoustics have significantly impacted various aspects of this field, such as source localization, target recognition, communication, and geoacoustic inversion. This review provides a comprehensive summary and evaluation of these developments. As a data-driven approach, ML played a pivotal role in discerning intricate relationships between input features and desired labels based on the provided training dataset. They are achieving success in ocean acoustic applications through ML hinges on several critical factors, including well-designed input feature preprocessing, appropriate labels, choice of ML models, effective training strategy, and availability of ample training and validation datasets. This review highlights noteworthy results from published studies to illustrate the effectiveness of ML methods in diverse application scenarios. In addition, it delves into the essential techniques employed within these applications. To understand the utility of ML in underwater acoustics, one must analyze its advantages and limitations. This assessment will aid in identifying scenarios where ML excels and those where it may face challenges. In addition, it provides insights into promising avenues for future research, shedding light on potential research directions that warrant exploration.

This paper proposes the use of gated feedback gated recurrent unit network (GFGRU), a learning-based sparse estimation algorithm, for multiple source localization in the direct arrival zone of the deep ocean. The GFGRU, trained on sound field replicas of a single source generated by an acoustic propagation model, is used to estimate the ranges and depths of multiple sources without knowing the number of sources. The performance of GFGRU is compared to the Bartlett processor, feedforward neural network (FNN), and sparse Bayesian Learning (SBL) algorithm. Simulations indicate that GFGRU behaves similarly to SBL and offers modest localization performance improvement over the Bartlett and FNN in the presence of array tilt mismatch. The results of real data from the South China Sea also verify the robustness of the proposed GFGRU using a 105 m-aperture vertical array in the deep ocean.

In the direct arrival zone of the deep ocean, the multi-path time delays have been used for acoustic source localization. One of the challenges in conventional localization methods is to artificially determine which paths the extracted delays belong to. A convolutional neural network, taking the autocorrelation functions as the input feature directly, is proposed for source localization to avoid the path determination procedure. Since some multi-path arrivals may not be visible due to absorption in the bottom of the ocean, a data augmentation method based on a ray propagation model is proposed. Tests on simulated and real data validate the method.

Deep-learning source localization methods trained on synthetic replicas calculated by acoustic propagation models (referred to as model-based source localization methods via deep learning) suffer from environmental mismatches between replicas and measurements, which degrade the localization performance. This article introduces deep transfer learning (DTL), specifically the unsupervised domain adaptation (UDA) and fine-tuning methods for source localization using ships of opportunity (SOO) to alleviate the influence of mismatched replicas. First, the range estimation network is trained using simulated replicas and unlabeled SOO data. Subsequently, the parameters of the ranging network are transferred to a depth estimation network and fine-tuned accordingly. Simulation results demonstrate that the proposed method (UDA-DTL) exhibits greater robustness to array tilt and sound speed profile mismatches in the direct arrival zone compared to the Bartlett processor and the ResNet estimator. Experimental data from the South China Sea also verified the validity of the proposed UDA-DTL estimator. The results demonstrate that supplementing model-based supervised learning with unlabeled SOO, the impact of unknown uncertainty on the localization performance is alleviated. This study highlights the potential to utilize unlabeled Big Data in applications of underwater source localization.