Smeared spectrum jamming suppression for radar based on signal separation

Template matching Smeared spectrum jamming can lead to the generation of a large number of false targets in radar, which is one of the common types of mainlobe jamming in radar systems. How to suppress such interference is an important research topic in the field of radar anti-jamming domain. In this paper, we propose an anti-mainlobe smeared spectrum jamming method based on signal separation. Utilizing Fourier decomposition of instantaneous amplitude, a sparse, nonparametric model for the observation of received signals has been constructed. This formulation transforms the interference suppression into a signal separation problem. By performing joint estimation of instantaneous amplitude (IA) and instantaneous frequency (IF), we can accurately reconstruct and separate the interference from radar echoes. Specifically, we adopt an alternating iterative strategy to refine the estimates of IA and IF. For the estimation of the IA, the Expectation- Maximization-Gaussian Mixture-Approximate Message Passing-Model Order Selection (EM-GM-AMP-MOS) method is employed based on the sparse observation model. Further, the estimate of IF is achieved through establishing an updating model using the smoothness characteristic of the IF. Subsequently, the interference is reconstructed utilizing the estimations of IA and IF. Simulation results illustrate that the proposed method adeptly suppresses smeared spectrum jamming and enhances the Signal-to-Jamming-Plus-Noise Ratio (SJNR) of the radar.