为有效降低多输入多输出(MIMO)雷达稀疏天线阵列的峰值旁瓣电平,提出一种粒子群算法与遗传算法相结合的混合布阵方法。该方法充分发挥粒子群算法的收敛性以及遗传算法中种群的多样性,并提出一种新的判断种群是否存在“早熟”趋势的标志,实现交叉概率和变异概率的非线性自适应调节,避免传统遗传算法存在的“早熟”现象。通过各算法对MIMO雷达发射天线和接收天线的位置进行优化比较,获得更低的MIMO雷达天线方向图综合中的峰值旁瓣电平。仿真结果表明:与基本的遗传算法和粒子群算法以及其他改进的自适应遗传算法相比较,新算法具有更快的收敛速度和更可靠的稳定性。
A hybrid approach was proposed to synthesize the MIMO radar antenna arrays with low peak side lobe levels (PSLLs). Particle swarm optimization (PSO) and genetic algorithm (GA) were mixed into a hybrid approach, which can make full use of the advantages of each to find the optimal array arrangements. In order to resolve the problem that traditional GA was prone to be "premature", an improved adaptive operator was proposed, and the adaptive adjustments of crossover probability and mutation probability were realized. The results show that the optimal PSLLs can be achieved by hybrid approach. The hybrid approach with the adaptive operator produces more satisfactory results on the best value, and shows that the new method has fast convergence and higher robustness than the particle swarm optimization and genetic algorithm and other documents presented different adaptive operators.