Underwater target recognition is a research component that is crucial to realizing crewless underwater detection missions and has significant prospects in both civil and military applications. This paper provides a comprehensive description of the current stage of deep-learning methods with respect to raw hydroacoustic data classification, focusing mainly on the variety and recognition of vessels and environmental noise from raw hydroacoustic data. This work not only aims to describe the latest research progress in this field but also summarizes three main elements of the current stage of development: feature extraction in the time and frequency domains, data enhancement by neural networks, and feature classification based on deep learning. In this paper, we analyze and discuss the process of hydroacoustic signal processing; demonstrate that the method of feature fusion can be used in the pre-processing stage in classification and recognition algorithms based on raw hydroacoustic data, which can significantly improve target recognition accuracy; show that data enhancement algorithms can be used to improve the efficiency of recognition in complex environments in terms of deep learning network structure; and further discuss the field’s future development directions.

Ship noise analysis is a critical area of research in hydroacoustic remote sensing due to its practical implications in identifying ship direction, type, and even specific ship identities. However, the limited availability of data poses challenges in developing accurate ship noise classification models. Previous studies have mainly focused on small-sample learning approaches, resulting in complex network structures. Nonetheless, underwater robots often have limited computing power, making it essential to develop simpler recognition networks. In this paper, we address the issue of data scarcity by introducing positive incentive noise. We propose a CNN-based hydroacoustic signal recognition method that achieves comparable or superior performance to previous studies, using a simple network structure as a back-end decision system. We describe the feature extraction process using a dataset with added noise and compare the performance of various features. Additionally, we compare our proposed method with previous studies. Experimental results demonstrate that simple neural networks can achieve high performance and excellent generalizability without the need for complex network structures like adversarial learning models.

Pulse noise (such as glacier fracturing and offshore pile driving), commonly seen in the marine environment, seriously affects the performance of Direction-of-Arrival (DOA) estimation methods in sonar systems. To address this issue, this paper proposes a high robustness underwater target estimation method based on variational sparse Bayesian inference by studying and analyzing the sparse prior assumption characteristics of signals. This method models pulse noise to build an observation signal, completes the derivation of the conditional distribution of the observed variables and the prior distribution of the sparse signals, and combines Variational Bayes (VB) theory to approximate the posterior distribution, thereby obtaining the recovered signal of the sparse signals and reducing the impact of pulse noise on the estimation system. Our simulation results showed that the proposed method achieved higher estimation accuracy than traditional methods in both single and multiple snapshot scenarios and has practical potential.

Miniaturized detection devices in the ocean generally experience problems such as short endurance and unreliable power supplies. This article aimed to develop a dynamic ocean temperature difference energy collection device to capture ocean temperature difference energy and provide objective electricity for stable detection devices. The main focus was to conduct experimental research on the effectiveness of a dynamic ocean temperature difference energy power generation device. During the research process, the fact that ammonia gas in a working fluid is easy to liquefy and vaporize was utilized. By utilizing the increase in seawater temperature during the floating process of the device, it vaporized and drove the turbine to rotate for power generation. In the structural design, multiple sets of small air chambers were creatively proposed, which could effectively control the air pressure and improve the stability of the airflow. By charging the airflow to impact the turbine, multiple sets of power generation fans were used to form a stable current. Further, the buoyancy of the device could be changed by adding phase change materials between the air chamber and the device shell, and the temperature difference between the two ends of the phase change materials could be used to change the electron density of the material to form a weak current. In this experimental study, concepts such as the structural design of multiple small gas chambers, miniaturization of energy collection devices, compensation power generation of phase change materials, and application scenarios of devices combined with Argo buoys were all proposed for the first time. The results of this experimental study indicate that the overall power generation of the device is about 2A, and its maximum output power amplitude is about 22 W. The cyclic thermal efficiency of the power generation device can be increased from +0.19% to +0.88%. The development of this thermoelectric power generation device can provide a considerable stable power supply for ocean observation devices, especially the buoy device represented by Argo, which can extend the endurance of deep-sea exploration devices.