An Anti-Mainlobe Suppression Jamming Method Based on Improved Blind Source Separation Using Variational Mode Decomposition and Wavelet Packet Decomposition

Mainlobe suppression jamming significantly degrades radar detection performance. The conventional blind source separation (BSS) algorithms often fail under high-jamming-to-signal-ratio (JSR) and low-signal-to-noise-ratio (SNR) conditions. To overcome this limitation, we propose an enhanced BSS method combining variational mode decomposition (VMD) and wavelet packet decomposition (WPD), termed VMD-WPD-JADE. The proposed approach first applies VMD-WPD for noise reduction in radar signals and then utilizes the JADE algorithm to compute the separation matrix of the denoised signals, effectively achieving blind source separation of radar echoes for interference suppression. We evaluate the method using noise-amplitude modulation and noise-frequency modulation jamming scenarios. The experimental results show that at a JSR = 50 dB and an SNR = -5 dB, our method successfully separates the target signals. Compared with the conventional blind source separation (BSS) algorithms, the proposed technique demonstrates superior robustness, achieving a 4-11% improvement in the target detection probability under noise-amplitude modulation (NAM) jamming and a 4-16% enhancement under noise-frequency modulation (NFM) jamming within a signal-to-noise ratio (SNR) range of -5 dB to 5 dB.