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Intelligent Recognition of Underwater Acoustic Target Noise on Underwater Glider Platform

机译：水下滑翔机平台水下声学目标噪声的智能识别

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The underwater acoustic target detection system based on the underwater glider platform requires the platform itself to have the ability of target automatic tracking, identification and evaluation, but the traditional methods of underwater target noise identification have strong human-computer interaction characteristics. It can not meet the needs of automatic target recognition. In order to solve this problem, the feature extraction method, automatic recognition model had been studied with the combination of the characteristics of underwater glider in this paper. An intelligent recognition model of underwater target noise based on long-short terms memory network is established. The model is tested in the laboratory, and it is verified by the underwater acoustic signal obtained on the sea. The results show that the intelligent identification model of underwater acoustic target noise can identify underwater acoustic target noise without artificial participation, and meet the requirements of underwater acoustic target detection and identification of the underwater glider platform.
机译：基于水下滑翔机平台的水下声学目标检测系统要求平台本身具有目标自动跟踪，识别和评估的能力，但传统的水下目标噪声识别方法具有强大的人机相互作用特征。它无法满足自动目标识别的需求。为了解决这个问题，采用本文水下滑翔机特性的组合研究了特征提取方法，自动识别模型研究。建立了基于长短短术语存储网络的水下目标噪声智能识别模型。该模型在实验室中进行了测试，并且通过在海上获得的水下声学信号来验证。结果表明，水下声学目标噪声的智能识别模型可以识别水下声学目标噪声，无需人工参与，并满足水下声学目标检测和水下滑翔机平台的识别要求。

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