基于改进粒子群算法的平面阵同时多波束赋形方法

doi:10.12305/j.issn.1001-506X.2022.06.04

摘要/Abstract

摘要：

针对当下平面阵同时多波束赋形需求, 提出基于改进粒子群算法的平面阵同时多波束赋形方法。将自适应操作粒子结构、反梯度加权用于粒子群搜索算法作为创新点, 根据粒子群收敛度自适应调整操作粒子结构的概率, 以平衡粒子群全局探索和局部利用能力, 根据多目标优化程度对各优化指标反梯度加权, 以抑制多目标优化失衡, 对阵列相位、幅度优化实现低旁瓣、窄波束宽度和高增益的效果。实验表明, 在传统非线性智能优化方法效果不好的情况下, 该方法对阵元数较多的任意平面阵同时多波束赋形效果较好。

关键词: 平面阵, 同时多波束赋形, 粒子群算法, 自适应操作粒子结构, 反梯度加权

Abstract:

To meet the requirements of the current planar array simultaneous multi-beam forming, a simultaneous multi-beam forming method of planar array based on improved particle swarm algorithm is proposed. Adaptive operation particle structure and anti-gradient weighting are used in particle swarm optimization algorithm as innovation points. The probability of operation particle structure is adaptively adjusted according to the convergence degree of particle swarm to balance the global exploration and local utilization ability of the particle swarm. According to the degree of multi-objective optimization, each optimization index is weighted to suppress the imbalance of multi-objective optimization, and the phase and amplitude are optimized to obtain low sidelobe, narrow beam width and high gain. The experiment results and comparisons with well-konwn nonlinear intelligent optimization multi-beam forming methods show the method has better effect on multi beam forming of arbitrary planar array with more elements.

Key words: planar array, simultaneous multi-beam forming, particle swarm algorithm, adaptive operation particle structure, anti-gradient weighting

中图分类号:

TN985.92

魏法, 杨明磊, 何小静, 周鼎森, 陈伯孝. 基于改进粒子群算法的平面阵同时多波束赋形方法[J]. 系统工程与电子技术, 2022, 44(6): 1789-1797.

Fa WEI, Minglei YANG, Xiaojing HE, Dingsen ZHOU, Baixiao CHEN. Simultaneous multi-beam forming method for planar array based on improved particle swarm algorithm[J]. Systems Engineering and Electronics, 2022, 44(6): 1789-1797.

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算法	平均增益/ dB	俯仰波束宽度/(°)	方位波束宽度/(°)	旁瓣电平/ dB
IPSO1	28.54	10.0	13.50	－4.665 0
IPSO2	24.78	16.0	13.00	－4.464 0
GA	28.35	11.5	12.75	－4.093 5
GSA	32.00	10.0	13.50	－9.900 0
IPSO3	33.53	10.0	12.25	－12.345 0

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