Recognition of radar active-jamming through convolutional neural networks

• Main Authors: Yafeng Wang, Boye Sun, Ning Wang
• Format: Article
• Language: English
• Published: Wiley 2019-09-01
• Series: The Journal of Engineering
• Subjects: electronic countermeasures; radar computing; learning (artificial intelligence); radar signal processing; image classification; neural nets; jamming; numerous jamming; recognition jamming; radar researchers; convolutional neural networks; active jamming; anti-jamming process; radar active-jamming; radar application; rapidly developed radar countermeasures; radar many resources
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0659

Radar application in modern warfare becomes more and more rigorous because of the rapidly developed radar countermeasures, especially active jamming in recent years. It costs a radar many resources for anti-jamming in order to detect a target. Hence, it is of great value to recognise the active jamming and thereafter take measures to distinguish target from the numerous jamming. Traditional methods of recognition jamming are blamed for its low efficiency and low accuracy. Radar researchers are looking forward to a new way to do the recognition work. Machine learning has made great advancements in many areas such as image classification, language translation, signal processing and many other recognition tasks, due to its great performance and high accuracy. The authors applied a machine learning method, i.e. convolutional neural networks, to recognise active jamming here. The authors’ results demonstrate that convolutional neural networks have strong ability to distinguish active jamming and thus provide them adequate preparation for anti-jamming process.