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.

Mainlobe interference can severely degrade the performance of distributed phased-array radar systems in the presence of strong jamming or low-reflectivity targets. This paper introduces a signal–data dual-domain cooperative antijamming and localization (SDCAL) framework that integrates adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal-level denoising and separation. Following source separation, CFAR-based pulse compression is applied for precise range estimation, and multi-node data fusion is then used to achieve three-dimensional target localization. Under low signal-to-noise ratio (SNR) conditions, the adaptive CEEMDAN–WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, thereby accelerating convergence of the separation matrix. The subsequent pulse compression and CFAR detection steps provide reliable inter-node distance measurements for accurate fusion. The simulation results demonstrate that, compared to conventional blind-source-separation methods, the proposed framework markedly enhances interference suppression, detection probability, and localization accuracy—validating its effectiveness for robust collaborative sensing in challenging jamming scenarios.