改进算术优化算法用于稀布平面阵列综合

doi:10.19665/j.issn1001-2400.2023.03.019

西安电子科技大学学报 ›› 2023, Vol. 50 ›› Issue (3): 202-212.doi: 10.19665/j.issn1001-2400.2023.03.019

• 信息与通信工程 & 电子科学与技术 • 上一篇

改进算术优化算法用于稀布平面阵列综合

国强^1,²(),刘从业^1,²(),王亚妮^1,²(),王勇^1,²(),CHERNOGOR Leonid³()

1.哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨 150001
2.先进船舶通信与信息技术工业和信息化部重点实验室,黑龙江哈尔滨 150001
3.乌克兰哈尔科夫国立大学空间无线电物理系,乌克兰哈尔科夫 61022

收稿日期:2022-07-19 出版日期:2023-06-20 发布日期:2023-10-13
通讯作者: 王亚妮
作者简介:国强(1972—),男,教授,E-mail:guoqiang@hrbeu.edu.cn；|刘从业(1997—),男,哈尔滨工程大学硕士研究生,E-mail:lcy@hrbeu.edu.cn；|王勇(1974—),男,讲师,E-mail:wangyong@hrbeu.edu.cn；|CHERNOGOR Leonid(1950—),男,教授,E-mail:leonid.f.chernogor@gmail.com
基金资助:
国家重点研发计划(2018YFE0206500);国家自然科学基金面上项目(62071140);自由探索项目(3072022CF0801)

Improved arithmetic optimization algorithm for sparse planar arrays synthesis

GUO Qiang^1,²(),LIU Congye^1,²(),WANG Yani^1,²(),WANG Yong^1,²(),CHERNOGOR Leonid³()

1. College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China
2. Key Laboratory of Advanced Marine Communication and Information Technology,Ministry of Industry and Technology,Harbin 150001,China
3. Department of Space Radiophysics,V.N.Karazin Kharkiv National Univrsity,Kharkov 61022,Ukraine

Received:2022-07-19 Online:2023-06-20 Published:2023-10-13
Contact: Yani WANG

摘要/Abstract

摘要：

针对稀布平面阵列方向图旁瓣电平抑制和零陷综合问题,提出了一种基于改进算术优化算法的阵列天线综合算法。首先,为平衡开发和勘探过程比重,对算术优化算法中的算术优化加速器采用非线性函数重构;其次,采用前三优的个体代替当前最优个体进行勘探开发,并引入精英变异策略,以增强算法跳出局部最优的能力,提高算法的收敛精度;最后,提出了一种自适应矩阵映射法则,对当前阵元分布进行判断,若其不满足最小阵元间距约束,则通过调整策略对其进行调整,在避免不可行解的同时保证了阵元的自由度。与现有文献中的算法相比,改进的算术优化算法对单峰和多峰标准测试函数的优化精度和稳定性均有一定的提高;在稀布平面阵列旁瓣电平抑制实验和零陷综合实验中,所提算法可以综合出更优的峰值旁瓣电平和零陷深度,证明了所提算法的有效性。

关键词: 阵列综合, 稀布阵列, 矩阵映射法则, 算术优化算法

Abstract:

An array antenna synthesis algorithm based on an improved arithmetic optimization algorithm is proposed to address the problems of sidelobe level suppression and null steering synthesis of sparse planar array radiation pattern.First,the math optimizer accelerated in the arithmetic optimization algorithm is reconstructed using a nonlinear function to balance the exploitation and exploration process weights.Second,top three best individuals are used instead of the current best optimal individuals for exploration and exploitation and an elite variation strategy is introduced to enhance the ability of the algorithm to escape from the local optimum and improve the convergence accuracy of the algorithm.Finally,an adaptive matrix mapping law is proposed to judge the current array element distribution,and if it does not satisfy the minimum array element spacing constraint,it is adjusted by an adjustment strategy to avoid infeasible solutions while ensuring the degrees of freedom of the array element.Compared with the existing algorithms in the literature,the improved arithmetic optimization algorithm has improved the optimization accuracy and stability of both single-peak and multi-peak standard test functions; In the experiments of sparse planar array sidelobe level suppression and null synthesis,the proposed algorithm can synthesize a better peak sidelobe level and null depth,which proves the effectiveness of the proposed algorithm.

Key words: array synthesis, sparse arrays, matrix mapping laws, arithmetic optimization algorithms

中图分类号:

TN92

国强,刘从业,王亚妮,王勇,CHERNOGOR Leonid. 改进算术优化算法用于稀布平面阵列综合[J]. 西安电子科技大学学报, 2023, 50(3): 202-212.

GUO Qiang,LIU Congye,WANG Yani,WANG Yong,CHERNOGOR Leonid. Improved arithmetic optimization algorithm for sparse planar arrays synthesis[J]. Journal of Xidian University, 2023, 50(3): 202-212.

图/表 14

图1

图2

表1

基准测试函数"

函数	搜索空间
f₁= $∑ i = 1 D x i 2$	$- 100,100$
f₂= $∑ i = 1 D x i$ + $∏ i = 1 n x i$	$- 10,10$
f₃= $∑ i = 1 D ∑ j = 1 i x j 2$	$- 100,100$
f₄= $∑ i = 1 D - 1 100 (x i 2 - x i + 1) 2 + (1 - x i) 2$	$- 30,30$
f₅= $∑ i = 1 D x i 2 - 10 c o s (2 π x i) + 10$	$- 5.12,5.12$
f₆=-20exp $- 0.2 1 D ∑ i = 1 D x i 2 1 / 2$ -exp $1 D ∑ i = 1 D c o s (2 π x i)$ +20+e	$- 32,32$
f₇=1+ $14000 ∑ i = 1 D x i 2$ - $∏ i = 1 D$ cos $x i (i) 1 / 2$	$- 600,600$

表1

表2

图3

图4

图5

图6

图7

表3

表4

图8

图9

图10

参考文献 17

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	CHEN Zhengyu, LIU Zhengping, XIE Julan, et al. An Optimization Method of Rectangular Sparse Array Based on Self-Adjusting Mapping Rule[J]. Modern Radar, 2022, 44(5):12-18.
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函数	准则	AOA	DAOA	EHAOA	IAOA
f₁	平均值	1.01×10^-6	0.16×10^-7	1.41×10^-19	0
	标准差	3.51×10^-7	1.01×10^-7	2.56×10^-23	0
f₂	平均值	4.12×10^-4	2.06×10^-6	6.77×10^-17	0
	标准差	6.44×10^-4	2.17×10^-6	1.35×10^-16	0
f₃	平均值	2.69×10^-4	6.80×10^-5	4.85×10^-5	0
	标准差	3.04×10^-4	4.15×10^-5	9.15×10^-5	0
f₄	平均值	27.39	26.70	25.78	24.94
	标准差	0.28	0.27	0.74	0.29
f₅	平均值	4.00×10^-7	1.16×10^-9	1.21×10^-13	0
	标准差	3.43×10^-7	3.05×10^-9	1.02×10^-13	0
f₆	平均值	1.87×10^-4	2.84×10^-5	3.08×10^-13	0
	标准差	9.65×10^-5	3.07×10^-5	4.59×10^-13	0
f₇	平均值	8.27×10^-4	7.43×10^-4	0	0
	标准差	3.40×10^-3	2.30×10^-3	0	0

方法	适应度/dB		适应度/dB
方法	φ=0°	φ=90°	适应度/dB
DPMM	-32.32	-26.08	-58.40
IWO	-31.80	-25.10	-56.90
IAOA	-34.78	-26.69	-61.47

方法	NDL/dB		PSLL/dB
方法	θ=15°	θ=45°	PSLL/dB
SOS	-69.98	-70.01	-31.93
MOFO	-69.99	-70.00	-31.14
IAOA	-70.21	-72.98	-32.68

改进算术优化算法用于稀布平面阵列综合

Improved arithmetic optimization algorithm for sparse planar arrays synthesis

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