Jamming Classification Using Convolutional Neural Network-Based Joint Multi-domain Feature Extraction

‍ ‍Interference recognition technology is a key technology in the intelligent anti-interference communication system. Interference identification can provide a decision-making basis for the system to generate the best anti-interference method by accurately distinguishing the type of interference in the received signal. Aiming at the identification of typical suppression interference in wireless communication systems， this paper proposes an interference identification algorithm based on Convolutional Neural Network-based Joint Multi-Domain Feature Extraction （CNN-JMDFE）. Two types of pre-processing enhanced data， time-frequency images and frequency-domain sequences， are extracted through CNN at the same time， which effectively utilizes the advantages of the two data objects and improves the performance of interference recognition. The simulation results show that， in the case of dynamics and random parameters of interference， the CNN-JMDFE algorithm can accurately identify 14 types of single interference when the Jamming-to-Noise Ratio （JNR） ≥ -2 dB， and the recognition performance is significantly better than that of a single data format based on time-frequency images or frequency-domain sequences by Automatic Feature Extraction based on Convolutional Neural Network （AFE-CNN） algorithm. Compared with traditional manual feature extraction-based Deep Neural Network （MFE-DNN）， the proposed method significantly improves the classification accuracy under low JNR and enhances the anti-noise performance of interference features. For composite interference， the proposed algorithm can accurately classify 10 types of composite interference when JNR ≥ 0 dB.