As an important tool for monitoring the marine environment, safeguarding maritime rights and interests and building a smart ocean, underwater equipment has developed rapidly in recent years. Due to the problems of seawater corrosion, excessive deep-sea static pressure and noise interference in the marine environment and economy, the applicability of manufacturing materials must be considered at the beginning of the design of underwater equipment. Piezoelectric metamaterial is widely used in underwater equipment instead of traditional materials because the traditional materials can not meet the application requirements. In this paper, according to the application range of piezoelectric metamaterials in underwater equipment, the current application of piezoelectric metamaterials is reviewed from the aspects of sound insulation and energy conversion. On this basis, the future development prospect of piezoelectric metamaterials in underwater equipment is introduced.

This paper researches the recognition of modulation signals in underwater acoustic communication, which is the fundamental prerequisite for achieving noncooperative underwater communication. In order to improve the accuracy of signal modulation mode recognition and the recognition effects of traditional signal classifiers, the article proposes a classifier based on the Archimedes Optimization Algorithm (AOA) and Random Forest (RF). Seven different types of signals are selected as recognition targets, and 11 feature parameters are extracted from them. The decision tree and depth obtained by the AOA algorithm are calculated, and the optimized random forest after the AOA algorithm is used as the classifier to achieve the recognition of underwater acoustic communication signal modulation mode. Simulation experiments show that when the signal-to-noise ratio (SNR) is higher than −5dB, the recognition accuracy of the algorithm can reach 95%. The proposed method is compared with other classification and recognition methods, and the results show that the proposed method can ensure high recognition accuracy and stability.

Underwater magnetic surveying serves as the fundamental prerequisite for detecting sensitive underwater targets and magnetically susceptible submerged objects. However, when utilizing magnetic gradient tensor measurements for underwater positioning, the stability of the measurement apparatus can be significantly affected by hydrodynamic disturbances in the underwater environment, thereby having a substantial impact on data quality. Autonomous Underwater Vehicles (AUV) are unmanned underwater robots designed to independently perform various tasks and operations in underwater environments. In order to ensure the quality of data collection, this paper proposes a structure utilizing an Autonomous Underwater Vehicles platform equipped with a three-component magnetic gradiometer. This structure employs second-order tensor positioning algorithms and vertical gradient positioning algorithms, coupled with the Autonomous Underwater Vehicles’s inherent vertical profile motion, to effectively achieve the precise positioning of underwater cables. Simulation results indicate that, in the absence of geomagnetic background noise, both horizontal and vertical structures yield favorable positioning results. However, when introducing background noise of 40,000 nT, the horizontal structure exhibits larger positioning errors, whereas the vertical structure demonstrates smaller errors. Experimental results show that in near-field scenarios, both structures achieve relatively similar positioning accuracy. Nevertheless, under identical distances in the far field, the vertical structure reduces errors by a minimum of 30.78% compared to the horizontal structure, thereby confirming the feasibility of integrating magnetic gradient tensor measurement structures with Autonomous Underwater Vehicles platforms.