A Subarray Level Adaptive Interference Suppression Method Based on Particle Swarm Optimization and Greedy Algorithm

For large array radar, it is very important to optimize subarray partition and adopt adaptive digital beamforming to resist interference. The existing algorithms still have some shortcomings, such as anti-interference effect, computing time and robustness. Aiming at overcoming these drawbacks, this manuscript proposes a subarray level adaptive interference suppression method for large arrays radar based on particle swarm optimization (PSO) and greedy algorithm. In this paper, the optimization process is divided into three steps: establishment on objective function, subarray partition and optimization for the subarray weighted value. Among them, we firstly transform the multi-objective optimization problem into a single-objective optimization problem, and then optimize subarray partition by column and row of the partition matrix, and further optimize the partition matrix by greedy algorithm. Finally an analytical method is used to optimize the subarray weighted value. The simulation results show that this method is effective for anti-interference on array radar, and it can get high SINR and fast computing speed, when compared with other existing methods. Meanwhile, the subarray partition result obtained by this method is also more simple and reasonable than other methods. In addition, the anti-interference of this method keeps good robustness.