基于混合策略优化粒子群的阵列天线干扰抑制算法OA

In view of the unsatisfactory interference suppression performance of the traditional linearly constrained minimum variance(LCMV)beamforming under low snapshots and coherent source conditions,this paper proposes an improved particle swarm optimization(PSO)algorithm.In this method,the weight vector is optimized by constructing an anti-interference fitness function,and multiple hybrid strategies are introduced to enhance global optimization capability and convergence speed.Simulations demonstrate that the proposed algorithm exhibits precise convergence and maintains robust anti-interference performance even under low snapshot conditions,effectively suppressing both coherent and broadband interference signals.In comparison with the conventional algorithms,the proposed algorithm achieves significant performance improvement in the aforementioned interference scenarios.The nonlinear weight optimization in interference suppression can be realized effectively.