基于机器学习的水下目标检测

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Pub Date : 2020-09-01 DOI:10.1109/ICICSP50920.2020.9232081

Wen Zhang, Yanqun Wu, Yonggang Lin, Lina Ma, Kaifeng Han, Yu Chen, Chen Liu

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引用次数: 5

摘要

水下目标检测是声信号处理的重要组成部分。它主要由坏信道检测、波束形成、分类、跟踪和定位四个部分组成。本文探讨了基于机器学习或深度学习实现不良信道检测、分类、跟踪和定位的可能性。它们都是单独实现的，并成功地使用了机器学习或深度学习。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Underwater Target Detection Based on Machine Learning

Underwater target detection is an important part of acoustic signal processing. It is mainly composed of bad channel detection, beamforming, classification, tracking and localization. In this paper, the possibility of realizing bad channel detection, classification, tracking and localization based on Machine Learning or Deep Learning was explored. And they were all implemented separately and successfully using Machine Learning or Deep Learning.

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来源期刊

2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP)

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