Radio jamming recognition algorithm based on MS-SSA and the CSA-CNN

Abstract

The application of wireless communication systems is continuously increasing across various fields. However, due to complex electromagnetic interference, these systems struggle to transmit data accurately, particularly in environments where Global Navigation Satellite Systems (GNSS) are unavailable. To ensure the accuracy of positioning information, it is essential to research antijamming techniques. Interference identification serves as a prerequisite for effective antijamming strategies. This paper proposes a jamming recognition algorithm based on multistep singular spectrum analysis (MS-SSA) and the channel-spatial attention convolutional neural network (CSA-CNN). Noisy jamming signals are filtered via the MS-SSA method to enhance the characteristics of jamming signals at low jamming-to-noise ratios (JNRs). After filtering, the CSA-CNN is employed for jamming recognition, incorporating multidomain feature parameters. The CSA-CNN integrates the global attention mechanism to enhance the model's ability to address significant jamming features, thereby improving recognition performance. The experimental results indicate that MS-SSA achieves a superior filtering effect compared with conventional methods such as the wavelet and Kalman algorithms. In identifying jamming signals, the recognition accuracy of the CSA-CNN can exceed 90% at JNR=-2 dB. The CSA-CNN achieves superior recognition performance and generalizability compared to the convolutional neural network (CNN) and multi-branch CNN (MB-CNN).