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X波段微带余割平方扩展波束天线阵赋形优化遗传算法研究
引用本文:张金玲,万文钢,郑占奇,甘曦,朱兴宇. X波段微带余割平方扩展波束天线阵赋形优化遗传算法研究[J]. 物理学报, 2015, 64(11): 110504-110504. DOI: 10.7498/aps.64.110504
作者姓名:张金玲  万文钢  郑占奇  甘曦  朱兴宇
作者单位:1. 北京邮电大学电子工程学院, 北京 100876;2. 中国科学研究院微电子研究所, 北京 100029
基金项目:国家自然科学基金(批准号:61171051)和北京市自然科学基金和北京市教育委员会科技计划重点项目(批准号:KZ201310028032)资助的课题.
摘    要:提出了一种改进型自适应遗传算法, 该算法用logistic函数拟合交叉概率和变异概率, 以赌轮盘选择和精英保留相结合的方式, 在全局寻找最优解. 与经典遗传算法相比, 改进型自适应遗传算法可以大大提高算法的求解质量. 本文基于改进的自适应遗传算法研究设计了-3 dB范围为0°-12°, -10 dB波束宽度为65°, 波束覆盖为65°, 天线频带范围为8.5-9.8 GHz, 中心频率为9.05 GHz的X波段微带余割平方扩展波束天线阵. 研究结果表明改进型自适应遗传算法对方向图的拟合程度具有较大提高, 适应度值可以从0.07以下提升到0.09以上.

关 键 词:自适应遗传算法  余割平方扩展波束  X波段天线阵
收稿时间:2014-11-15

Research on X band extended cosecant squared beam synthesis of micro-strip antenna arrays using genetic algorithm
Zhang Jin-Ling,Wan Wen-Gang,Zheng Zhan-Qi,Gan Xi,Zhu Xing-Yu. Research on X band extended cosecant squared beam synthesis of micro-strip antenna arrays using genetic algorithm[J]. Acta Physica Sinica, 2015, 64(11): 110504-110504. DOI: 10.7498/aps.64.110504
Authors:Zhang Jin-Ling  Wan Wen-Gang  Zheng Zhan-Qi  Gan Xi  Zhu Xing-Yu
Affiliation:1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;2. Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
Abstract:Synthesis of desired radiation patterns without an optimization algorithm is usually time consuming and inefficient. To achieve a desired radiation pattern such as cosecant squared beam and contoured beam, different evolutionary algorithms such as genetic algorithm (GA), particle swarm optimization algorithm, and invasive weed optimization algorithm have been used to find the excitation of radiation elements. Adaptive genetic algorithm (AGA) optimizer is a robust, stochastic search method, modeled on the principles and concepts of natural selection and evolution. As an optimizer, the powerful heuristic of the AGA is effective for solving complex and related problems. An improved AGA is proposed, in allusion to the characteristics of optimizing designs of antenna arrays which have many parameters and complicated structures. This algorithm constructs an adjustble formula to produce the crossover rate and mutation rate based on a logistic curve equation. In the way of combining roulette wheel selection and elitist strategy, this algorithm searches for the optimal solution in the global space, and is compared with the classical GA; the improved AGA has a better performance in seeking the solution. Taking the mutual coupling between the elements into account, we design the X band extended cosecant squared beam micro-strip antenna arrays based on the improved AGA. Specifications of the antenna are as follows:a -3 dB beam width in height is from 0° to 12°, a -10 dB beam width in height is from 12° to 65°, and a total height coverage is 65°; a frequency band ranges from 8.5 to 9.8 GHz and its center frequency is 9.05 GHz. Simulation results show that the fitness increases from 0.07 to 0.09, and the quality of the synthesized radiation pattern has a great improvement, which verifies the superiority of the improved AGA proposed in this paper. In addition, the prospect of the designed antenna which has an extended cosecant squared beam is promising in air-surveillance radar systems, where the radiation pattern of the antenna will compensate for the free-space loss.
Keywords:adaptive genetic algorithm  extended cosecant squared pattern  X band antenna array
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