Main-lobe Interference Suppression Method of Multistatic Radar Based on Adaptive Particle Swarm Optimization

Main-lobe suppression interference poses a significant challenge to radar detection performance. Leveraging its inherent spatial diversity, multistatic radar facilitates distinct differences in spatial correlation characteristics between target and interference signals. Based on this difference, this paper proposes a method for suppressing main-lobe interference in multistatic radar. Unlike traditional methods that estimate the interference signal subspace through covariance matrix decomposition, this proposed method innovatively models the estimation of the interference signal subspace as a global optimization problem based on the maximum output signal-to-interference-plus-noise ratio (SINR) criterion. This approach effectively mitigates the issue of inaccurate subspace estimation caused by interference leakage in traditional algorithms. To further address the underlying optimization problem, we propose an adaptive particle swarm optimization (APSO) algorithm. This algorithm achieves precise estimation of the interference subspace by iteratively refining the interference parameters, which significantly enhances both the convergence speed and the global optimization capability. Finally, main-lobe interference suppression is achieved through projection processing. Numerical experiments confirm the effectiveness of the proposed method in suppressing various types of main-lobe interference, demonstrating its superior performance relative to conventional algorithms.