基于改进的粒子群算法实现阶梯幅度量化相控阵天线的低副瓣

 从统计意义上逼近传统的连续加权分布，利用无约束优化方法求出一组最优量化台阶.在此基础上，提出处理加权宽度的改进约束整数粒子群优化(particle swarm optimization,PSO)算法,实现阶梯幅度量化加权.设计了处理量化幅度权值的Powell PSO混合算法，进一步降低峰值副瓣电平.首次设计星载降水测量雷达(precipitation radar, PR)相控阵天线的低副瓣,改进的PSO算法在寻优能力、算法鲁棒性方面都得到了增强.

Realization of Low Sidelobe for Step-Quantized Amplitude Phased Array Antennas Based on Modified Particle Swarm Optimization

This paper discusses the application of the particle swarm optimization (PSO) algorithm to accomplish low sidelobe for step quantized amplitude phased array antennas. A group of multi step quantized amplitude weights are obtained using an unconstrained optimization method, which approaches the traditional Taylor distribution statistically. An improved constrained PSO algorithm coping with step widths is used to achieve step quantized amplitude. The Powell PSO hybrid algorithm for quantized amplitude weights is proposed to lower the peak sidelobe level for antennas. Low sidelobe for spaceborne precipitation radar (PR) phased array antennas using the method is designed. The improved PSO algorithm has enhanced ability of optimization and better robustness.