Feature extraction of Follower Jamming Signals for Frequency-Hopping Based on Sparse Decomposition

A novel method is presented for feature extraction of follower jamming signals by using sparse decomposition technique. The basic principles of sparse decomposition are introduced. According to the difference of time delay in the time domain and amplitude in the frequency domain between the frequency hopping (FH) signals and follower jamming (FJ) signals, the marked difference feature extraction is obtained when the FH signals and FJ signals are reconstructed. The simulation results show that the recognition rate of FJ can achieve 95% when the SNR is greater than 5dB, and the feasibility and effectiveness of the method is verified.