雷达辐射源识别中的深度学习技术

IF 0.8 4区 工程技术 Q3 MULTIDISCIPLINARY SCIENCES
Preeti Gupta, Pooja Jain, O G Kakde
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引用次数: 0

摘要

在电子战领域,雷达信号的识别是电子情报的关键任务之一。在作战环境中,识别雷达发射体是非常重要的,无论是友方还是敌方,以便对其采取适当的雷达对抗措施。随着电磁环境的日益复杂和信号特征的多样性,高识别精度的雷达辐射源识别已成为一项极具挑战性的任务。在这种复杂的电磁环境下,传统的雷达识别方法显示出一定的局限性。随着人工神经网络的出现,出现了几种基于人工神经网络的雷达分类和识别方法,特别是深度学习方法。机器学习和深度学习算法现在经常被用来更准确、更稳健地从雷达信号中提取各种类型的信息。本文阐述了深度神经网络(DNN)在雷达辐射源分类和识别中的应用。由于深度学习方法能够准确地分类雷达信号中的复杂模式,因此它们在识别雷达发射器方面表现出了巨大的希望。通过对基于深度学习的方法进行全面的文献分析,本研究旨在帮助研究人员和从业人员更好地理解深度学习技术在雷达发射器分类和识别方面的应用。研究表明,深度神经网络可以成功地用于雷达分类和识别。 & # x0D;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Techniques in Radar Emitter Identification
In the field of electronic warfare (EW), one of the crucial roles of electronic intelligence is the identification of radar signals. In an operational environment, it is very essential to identify radar emitters whether friend or foe so that appropriate radar countermeasures can be taken against them. With the electromagnetic environment becoming increasingly complex and the diversity of signal features, radar emitter identification with high recognition accuracy has become a significantly challenging task. Traditional radar identification methods have shown some limitations in this complex electromagnetic scenario. Several radar classification and identification methods based on artificial neural networks have emerged with the emergence of artificial neural networks, notably deep learning approaches. Machine learning and deep learning algorithms are now frequently utilized to extract various types of information from radar signals more accurately and robustly. This paper illustrates the use of Deep Neural Networks (DNN) in radar applications for emitter classification and identification. Since deep learning approaches are capable of accurately classifying complicated patterns in radar signals, they have demonstrated significant promise for identifying radar emitters. By offering a thorough literature analysis of deep learning-based methodologies, the study intends to assist researchers and practitioners in better understanding the application of deep learning techniques to challenges related to the classification and identification of radar emitters. The study demonstrates that DNN can be used successfully in applications for radar classification and identification.
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来源期刊
Defence Science Journal
Defence Science Journal 综合性期刊-综合性期刊
CiteScore
1.80
自引率
11.10%
发文量
69
审稿时长
7.5 months
期刊介绍: Defence Science Journal is a peer-reviewed, multidisciplinary research journal in the area of defence science and technology. Journal feature recent progresses made in the field of defence/military support system and new findings/breakthroughs, etc. Major subject fields covered include: aeronautics, armaments, combat vehicles and engineering, biomedical sciences, computer sciences, electronics, material sciences, missiles, naval systems, etc.
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