DRFM-Based Jamming Signal Recognition Method Guided by Target Detection

Abstract

Digital radio frequency memory (DRFM) based jammer equipped on target can generate multiple false targets by intercepting and forwarding the signal transmitted by radar, which lead to a serious threat to the performance of a radar system. Therefore, the recognition of radar jamming signal type is a key step of radar countermeasure. The existing jamming recognition methods do not take into account the factor of real-time performance. Since it uses the raw time-domain signals and complex network structures, which incurs dense computational complexity. To tackle this problem, this paper analyzes the time-frequency distribution maps of jamming signals after signal processing, and proposes a radar jamming recognition method based on the guidance of target detection. In the proposed method, firstly, based on the target detection information, the time-frequency image of the jamming region is obtained by performing short-time Fourier transformation (STFT); Secondly, a lightweight convolutional neural network (CNN) model was constructed to achieve jamming recognition. Finally, effectiveness of this method was evaluated using the simulated datasets with six typical radar jamming signals. The results show that the proposed method performed well in accuracy and efficiency, which is practical for real-time anti-jamming.