Radar Jamming Recognition: Models, Methods, and Prospects

Abstract

In modern warfare with complex and changeable electromagnetic environments, radar jamming is getting more complex and realistic, which poses a serious threat to radar; jamming recognition has become a hot topic in the field of electronic countermeasures. To make effective antijamming measures, numerous jamming recognition methods have been proposed. This article presents a systematic review of jamming recognition for this topic. Specifically, first building a system framework for jamming models, including deception jamming, suppression jamming, and smart jamming, thoroughly explaining the operational mechanisms. Then, recognition methods based on traditional machine learning are summarized and are delved into the advantages and disadvantages of feature extraction methods and classifiers. Furthermore, the focus shifts to neural network-based methods, such as shallow neural network methods and deep neural network methods. In particular, restricted sample strategies are also discussed as potential future directions. Finally, conclusions on the current status of jamming recognition methods and the prospects for future work are made. This article provides a reference for the research of radar jamming recognition.

Keywords

• Feature extraction
• limited sample strategies
• neural network
• radar jamming recognition
• traditional machine learning