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.
Researchers use underwater acoustic equipment to explore the unknown ocean environment, which is one of the important means to understand and utilize the ocean. For underwater acoustic equipment, the application of underwater acoustic metamaterials is the premise to ensure and improve the performance of underwater acoustic communication, acoustic stealth, and sonar detection. Due to the limitations of mass density law and high hydrostatic pressure, traditional underwater acoustic materials cannot effectively absorb low-frequency sound waves and have low efficiency of elastic energy conversion. The sound absorption effect is poor under low frequency and high hydrostatic pressure. In recent years, with the development of acoustic metamaterials technology, all kinds of underwater acoustic metamaterials have also been proposed. Compared with sound waves propagating in the air, underwater sound is more difficult to control than air sound with the same frequency, so the design of underwater acoustic metamaterials is more complicated. This paper reviews the basic characteristics, development history of sound absorption, sound insulation decoupling, and underwater acoustic guided metamaterials, then the existing problems and the future development direction of underwater acoustic metamaterials are discussed.
Reverberation characteristics must be considered in the design of sonar. The research on reverberation characteristics is based on a large number of actual reverberation data. Due to the cost of trials, it is not easy to obtain actual lake and sea trial reverberation data, which leads to a lack of actual reverberation data. Traditionally, reverberation data are obtained by modeling the generation mechanism of seafloor reverberation. The usability of the models requires a large amount of actual seafloor reverberation data to verify. In terms of the reverberation modeling theory, scattering models are mostly empirical, computationally intensive and inefficient. In order to solve the above obstacles, we propose a shallow seafloor reverberation data simulation method based on the generative adversarial network (GAN), which uses a small amount of actual reverberation data as reference samples to train the GAN to generate more reverberation data. The reverberation data generated by the GAN are compared with that simulated by traditional methods, and it is found that the reverberation data generated by the GAN meet the reverberation characteristics. Once the network is trained, the reverberation data are generated with very little computation. In addition, the method is universal and can be applied to any sea area. Compared with the traditional method, this method has a simple modeling idea, less computation and strong universality. It can be used as an alternative method for sea trials to provide data support for the study of seafloor reverberation characteristics, and it has broad application prospects in antireverberation technology research and active sonar design.
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