Dense-repeated Jamming Suppression Algorithm Based on the Support Vector Machine for Frequency Agility Radar

Abstract

Dense-repeated jamming is highly related to the radar-transmitted signal, and it has suppression and deception jamming effects, which makes detecting the real target difficult for a radar system and seriously threatens the operational capability of radar. To solve this problem, an intelligent suppression method based on the Support Vector Machine (SVM) is proposed in this paper. The optimal SVM model is obtained through offline training on a random sample set to intelligently identify and classify targets and interference. Then, the interference sidelobe in the target range unit is further suppressed by smoothing filtering. Finally, high-resolution two-dimensional reconstruction is performed based on Compress Sensing (CS) theory to estimate the target parameter information. Simulation experiments and measured data processing results reveal that the proposed algorithm can effectively suppress dense-repeated jamming and accurately detect real targets in different scenarios.

Keywords

• electronic counter-measures (ecm)
• machine learning
• frequency agility radar
• dense repeated jamming
• support vector machine (svm)