Main Lobe Deception Jamming Suppression Approach via SVD and FrFT for LFM Radars
Zhenshuo Lei, Qizhe Qu, Liang Zhang, Hui Chen, Gaoqi Dou, Yongliang Wang
Abstract
To solve the problem that radar performance is seriously affected by mainlobe deception jamming, a jamming suppression approach using singular value decomposition (SVD) and the fractional Fourier transform (FrFT) is proposed. First, Hankel matrix rearrangement and SVD are performed for the signals in each coherent processing interval. The energy of jamming and of the radar targets is aggregated from the slow-time dimension. Ensemble learning is then used for its cognitive properties. The corresponding relationship between jamming and the singular values is judged, and the interference is initially suppressed. Finally, the FrFT is applied for each pulse signal. The linear frequency modulation signal energy is aggregated from the fast-time dimension, and jamming is suppressed a second time. The results of simulations show that the proposed approach can be used to detect targets more effectively than existing methods in scenarios involving multiple strong mainlobe deception jammers.