Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization
Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization
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DOI：
10.1109/tap.2005.851762
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发表时间：
2005-08-01
影响因子：
5.7
通讯作者：
Christodoulou, CG
中科院分区：
文献类型：
--
作者：
Khodier, MM;Christodoulou, CG
提出了一种基于粒子群优化（PSO）算法的具有最小旁瓣电平和零陷控制的线阵几何综合方法。粒子群算法是一种新发现的高性能进化算法，能够解决一般N维线性和非线性优化问题。与遗传算法、模拟退火算法等进化算法相比，粒子群优化算法更易于理解和实现，所需的数学预处理也更少。首先将阵列几何综合问题转化为一个以旁瓣电平抑制和/或零陷位置为目标的优化问题，然后用粒子群优化算法求解最优阵元位置。三个设计实例，说明使用粒子群算法，并在每个例子中的优化目标是很容易实现。通过与二次规划方法（QPM）的结果进行比较，验证了PSO算法的结果。
This paper describes the synthesis method of linear array geometry with minimum sidelobe level and null control using the particle swarm optimization (PSO) algorithm. The PSO algorithm is a newly discovered, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and simulated annealing, the PSO algorithm is much easier to understand and implement and requires the least of mathematical preprocessing. The array geometry synthesis is first formulated as an optimization problem with the goal of sidelobe level (SLL) suppression and/or null placement in certain directions, and then solved by the PSO algorithm for the optimum element locations. Three design examples are presented that illustrate the use of the PSO algorithm, and the optimization goal in each example is easily achieved. The results of the PSO algorithm are validated by comparing with results obtained using the quadratic programming method (QPM).