基于改进混合量子遗传算法的二相编码雷达波形优化

doi:10.11959/j.issn.1000-436x.2024022

通信学报 ›› 2024, Vol. 45 ›› Issue (1): 129-140.doi: 10.11959/j.issn.1000-436x.2024022

• 学术论文 • 上一篇

基于改进混合量子遗传算法的二相编码雷达波形优化

张余¹, 赵婧², 贾彦国¹^,³, 沈秀敏¹^,⁴

¹ 燕山大学信息科学与工程学院，河北秦皇岛 066004
² 燕山大学经济管理学院，河北秦皇岛 066004
³ 河北省计算机虚拟技术与系统集成重点实验室，河北秦皇岛 066004
⁴ 河北省软件工程重点实验室，河北秦皇岛 066004

修回日期:2024-01-04 出版日期:2024-01-01 发布日期:2024-01-01
作者简介:张余（1999- ），女，河北石家庄人，燕山大学博士生，主要研究方向为量子计算、组合优化问题
赵婧（1982- ），女，河北张家口人，燕山大学博士生，主要研究方向为量子组合优化
贾彦国（1971- ），男，河北唐山人，燕山大学教授、博士生导师，主要研究方向为编码理论、智能补货、量子计算
沈秀敏（1981- ），女，河北保定人，燕山大学讲师、硕士生导师，主要研究方向为编码理论、序列设计
基金资助:
国家自然科学基金资助项目(61601401);河北省自然科学基金资助项目(F2018203057);河北省高等学校科学技术研究基金资助项目(QN2021144);河北省创新能力提升计划基金资助项目(22567626H);河北省软件工程重点实验室基金资助项目(22567637H)

Binary phase-coded radar waveform optimization based on improved hybrid quantum genetic algorithm

Yu ZHANG¹, Jing ZHAO², Yanguo JIA¹^,³, Xiumin SHEN¹^,⁴

¹ School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
² School of Economics and Management, Yanshan University, Qinhuangdao 066004, China
³ The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao 066004, China
⁴ The Key Laboratory of Software Engineering of Hebei Province, Qinhuangdao 066004, China

Revised:2024-01-04 Online:2024-01-01 Published:2024-01-01
Supported by:
The National Natural Science Foundation of China(61601401);The Natural Science Foundation of Hebei Province(F2018203057);The Research and Technology in Higher Education of Hebei(QN2021144);The Innovation Capability Improvement Plan Project of Hebei Province(22567626H);Project of Hebei Key Laboratory of Software Engineering(22567637H)

摘要/Abstract

摘要：

针对当前雷达波形优化算法搜索策略单一、适用范围受限的问题，提出了一种基于改进混合量子遗传算法的二相编码雷达波形优化算法。所提算法采用了一种新的自适应旋转角度策略，根据迭代次数和余弦相似度动态调整旋转角度，提高了算法的收敛速度、全局搜索能力和求解质量。仿真结果表明，与遗传算法、基本量子遗传算法和混合量子遗传算法相比，对于包含了单峰、多峰和非凸优化问题的6个标准测试函数，所提算法在质量和资源消耗上均表现更好；对于二相编码雷达波形优化，证实了使用所提算法优化波形是可行和有效的。

关键词: 相位编码, 雷达波形, 量子遗传算法, 自适应旋转角度策略, 峰值旁瓣电平

Abstract:

To overcome the problem of single search focus and limited application scope of existing algorithm, a binary phase-coded radar waveform optimization (RWO) based on improved hybrid quantum genetic algorithm (IHQGA) was proposed.IHQGA adopted a novel self-adaptive rotation angle strategy, which dynamically adjusted the rotation angle based on evolutionary process and cosine similarity.The convergence speed, global search capability, and solution quality were improved.Simulation results demonstrate that compared with genetic algorithms, basic quantum genetic algorithms, and hybrid quantum genetic algorithms, IHQGA performs better in terms of solution quality and resource consumption for six benchmark functions that include single-peak, multi-peak, and non-convex optimization problems.Additionally, for binary phase-coded RWO, which verifies the feasibility and effectiveness of IHQGA in WO.

Key words: phase coding, radar waveform, QGA, self-adaptive rotating angle strategy, PSLL

中图分类号:

TN957

张余, 赵婧, 贾彦国, 沈秀敏. 基于改进混合量子遗传算法的二相编码雷达波形优化[J]. 通信学报, 2024, 45(1): 129-140.

Yu ZHANG, Jing ZHAO, Yanguo JIA, Xiumin SHEN. Binary phase-coded radar waveform optimization based on improved hybrid quantum genetic algorithm[J]. Journal on Communications, 2024, 45(1): 129-140.

图/表 13

图1

表1

自适应旋转角度调整策略"

$x_{i j}^{t}$	$b_{j}^{t - 1}$	$f (x_{i}^{t}) ＞ f (b_{}^{t - 1})$	$Δ θ_{i}^{t}$		$s (α_{i j}^{t}, β_{i j}^{t})$
$x_{i j}^{t}$	$b_{j}^{t - 1}$	$f (x_{i}^{t}) ＞ f (b_{}^{t - 1})$	$Δ θ_{i}^{t}$	$α_{i j}^{t} β_{i j}^{t} ＞ 0$	$α_{i j}^{t} β_{i j}^{t} ＜ 0$	$α_{i j}^{t} β_{i j}^{t} = 0$
0	0	True	$Δ θ_{i}^{t}$	-1	+1	±1
0	0	False	$Δ θ_{i}^{t}$	-1	+1	±1
0	1	True	$Δ θ_{i}^{t}$	-1	+1	±1
0	1	False	$Δ θ_{i}^{t}$	+1	-1	±1
1	0	True	$Δ θ_{i}^{t}$	+1	-1	±1
1	0	False	$Δ θ_{i}^{t}$	-1	+1	±1
1	1	True	$Δ θ_{i}^{t}$	-1	+1	±1
1	1	False	$Δ θ_{i}^{t}$	+1	-1	±1

表1

图2

表2

表3

表4

图3

图4

表5

图5

图6

图7

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算法	参数	取值
GA	多点交叉概率	P_c= 0.4
	多点交叉的交叉点个数	P_n= 4
	逆转变异概率	P_m=0.025
BQGA^[23]	固定旋转角度	Δθ=0 .1π
HQGA^[24]	固定旋转角度	Δθ=0 .1π
	量子非门变异概率	P_m=0.01
	全干扰交叉概率	P_c= 1
IHQGA	最大旋转角度	θ_max=0.15π
	最小旋转角度	θ_min=0.1π
	相似性权重	θ_sim=0.005π
	量子非门变异概率	P_m=0.01
	全干扰交叉概率	P_c= 1

函数	种群规模	基因数量	最大迭代次数	运行次数
F₁	100	50	100	50(35)
F₂	200	50(100)	100	50
F₃	100	50	200	50
F₄	100(200)	50(100)	100	50
F₅	100	50	100	50
F₆	100	50	100	50

函数	GA				BQGA
函数	均值	方差	最优值	时间/s	均值	方差	最优值	时间/s
F₁	0.009 97	0.000 47	1.07×10^-7	3.35	0.074 71	0.015 88	5.93×10^-6	3.69
F₂	0.077 05	0.010 07	0.000 16	8.30	0.025 33	0.001 02	1.79×10^-5	4.85
F₃	0.991 46	9.20×10^-6	0.999 93	8.89	0.990 77	4.23×10^-6	0.999 60	5.19
F₄	1.094 57	0.489 70	5.36×10^-5	4.24	1.970 67	1.760 66	0.003 84	2.98
F₅	177.391	363.54	186.730 9	1.97	171.487	434.906	186.693	3.56
F₆	0.996 44	0.001 923	1.031 624	0.56	0.825 67	0.084 37	1.031 55	2.58

函数	QGA				IHQGA
函数	均值	方差	最优值	时间/s	均值	方差	最优值最优值	时间/s
F₁	0.018 91	0.001 03	5.29×10^-7	3.39	1.40×10^-12	3.27×10^-23	1.78×10^-131.78×10^-13	5.73
F₂	0.013 21	0.000 70	9.96×10^-7	6.96	0.001 85	1.55×10^-5	7.54×10^-117.54×10^-11	6.97
F₃	0.992 16	1.22×10^-5	0.999 96	6.21	0.994 94	2.20×10^-5	11	7.46
F₄	1.205 70	1.218 70	9.61×10^-6	3.56	0.085 35	0.082 69	9.24×10^-129.24×10^-12	5.23
F₅	180.435	167.675	186.730 6	6.90	186.587	0.098 98	186.730 9186.730 9	7.28
F₆	0.957 62	0.012 42	1.031 626	4.69	1.019 95	0.000 49	1.031 6281.031 628	7.01

码长/位	GA			BQGA			HQGA			IHQGA
码长/位	旁瓣峰值	PSLL/dB	时间	旁瓣峰值	PSLL/dB	时间	旁瓣峰值	PSLL/dB	时间	旁瓣峰值	PSLL/dB	时间
95	13	-17.28	9.8 s	11	-18.73	15.6 s	9	-20.47	17.0 s	8	-21.49	16.6 s
250	21	-21.51	9.8 min	23	-20.72	13.7 min	18	-22.85	13.4 min	17	-23.35	12.2 min
300	27	-20.92	17.7 min	26	-21.24	16.1 min	20	-23.52	16.0 min	19	-23.97	18.2 min
400	31	-22.21	21.4 min	27	-23.41	35.8 min	26	-23.74	34.3 min	24	-24.44	32.2 min
800	49	-24.26	4.1 h	51	-23.91	4.2 h	44	-25.19	4.5 h	42	-25.60	3.9 h
1 024	56	-25.24	7.7 h	63	-24.22	7.2 h	50	-26.22	7.3 h	49	-26.40	6.7 h
1 500	80	-25.46	15.0 h	74	-26.14	14.5 h	69	-26.74	14.6 h	65	-27.26	14.3 h

基于改进混合量子遗传算法的二相编码雷达波形优化

Binary phase-coded radar waveform optimization based on improved hybrid quantum genetic algorithm

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