遗传二进制多粒子群优化算法及其在子阵STAP中的应用

Genetic Binary Multiple Particle Swarm Optimization Algorithm and Application in Subarray STAP

抑制杂波和干扰的子阵空时自适应处理的性能会受子阵方向图栅瓣的影响而严重下降,非均匀子阵划分和子阵权值设计可有效抑制天线方向图的栅瓣,消除STAP性能曲线上的栅凹口。由于子阵划分和子阵权值分别对应于整数和实数变量,使得该问题为一种特殊的混合优化问题。针对该特殊问题本文提出了一种新的遗传二进制多粒子群优化算法- GBMPSO。该算法不仅可处理二进制编码粒子,而且采取了多粒子群共同进化且在同一种群内部进行交叉和变异的机制,有效避免了算法早熟。针对四种典型测试函数的仿真实验表明,GBMPSO算法与二进制PSO算法和遗传算法相比在收敛速度和算法精度上均有明显的优势。最后,给出了该算法在子阵STAP中应用的实例。

The performance of subarray STAP used to mitigate clutter and interference will be degraded dramatically by grating lobes of subarray pattern. The grating lobes of antenna pattern and corresponding grating notches of STAP performance can be eliminated effectively by proper partition of subarrays and design of subarray weights. Because the optimization parameters corresponding to subarray partitions and weights are integer and real variables, respectively, the optimization problem is a particular mixture one. To solve this prob- lem,a new genetic binary multiple particle swarm optimization （GBMPSO） algorithm is proposed,which can deal with binary particles. In GBMPSO, two techniques are used to overcome the premature phenomenon. One is cooperative evolution within multiple swarms and the other is the crossover and mutation processes in each swarm. Results on the minimization of a set of four standard test functions show that the convergence rate and precision of the GBMPSO algorithm are much higher than those of binary PSO and genetic algorithm. Finally, an example is given to demonstrate its application in subarray STAP.