基于贪心遗传算法的液晶光学相控阵多光束扫描

doi:10.12382/bgxb.2022.1163

摘要/Abstract

摘要：

针对液晶光学相控阵单光束扫描周期长、捕获效率低的问题,提出基于贪心遗传算法的液晶光学相控阵多光束扫描方法。利用多个液晶光学相控阵实现互相独立扫描的多个光束,在用多光束对多个动态目标进行扫描时,根据目标已知的大小、位置以及速度信息,设计基于贪心遗传算法的多光束扫描方法,使用目标捕获效率作为目标函数,对每个光束分配待扫描的目标,并确定多个目标的扫描顺序;搭建液晶光学相控阵多光束扫描平台,在不同的目标数量、目标移动速度以及不同的光斑半径等条件下,对所提的扫描方法进行仿真以及实验验证。研究结果表明:相较于传统扫描方法,基于贪心遗传算法的多光束扫描方法不仅有效地缩短了扫描周期,而且提高了动态目标的捕获效率。

关键词: 液晶光学相控阵, 多光束扫描, 贪心遗传算法, 动态目标, 捕获效率

Abstract:

This paper presents a multi-beam scanning method for liquid crystal optical phased arrays (LC-OPAs) based on greedy genetic algorithm to solve issues of long scanning periods and low capture efficiency associated with single-beam scanning. Multiple LC-OPAs are utilized to generate independent beams with equal scanning areas. To efficiently scan and track multiple dynamic targets, a greedy genetic algorithm-based multi-beam scanning scheme is proposed. The scheme assigns targets to each beam based on target size, position, and velocity information, optimizing the scanning order to maximize target capture probability. A liquid crystal optical phased array multi-beam scanning platform is built, and the proposed scanning method is simulated and experimentally verified under various target numbers, target movement speeds, and spot radii. The results demonstrate that the proposed method can not only effectively reduces the scanning cycle but also improves the capture efficiency of dynamic targets.

Key words: liquid crystal optical phased array, multi-beam scanning, greedy genetic algorithm, dynamic target, capture efficiency

中图分类号:

TN249

叶闻语, 王春阳, 于金阳, 拓明侃, 王子硕. 基于贪心遗传算法的液晶光学相控阵多光束扫描[J]. 兵工学报, 2023, 44(9): 2650-2660.

YE Wenyu, WANG Chunyang, YU Jinyang, TUO Mingkan, WANG Zishuo. Multi-beam Scanning of Liquid Crystal Optical Phased Array Based on Greedy Algorithm[J]. Acta Armamentarii, 2023, 44(9): 2650-2660.

图/表 18

图1 液晶偏振光栅基本结构

Fig.1 Front view of liquid crystal polarization grating

图2 光束偏转示意图

Fig.2 Diagram of beam deflection

图3 LCHWP与LCPG组合结构

Fig.3 Combined structure of LCHWP and LCPG

图4 多光束生成系统

Fig.4 Multi-beam generation system

图5 16×16扫描光斑

Fig.5 16×16 scan light spots

图6 传统扫描方法的双光束扫描路径

Fig.6 Dual-beam scanning path of a traditional scanning method

图7 基于贪心遗传算法的多光束扫描流程

Fig.7 Multi-beam scanning process based on the greedy genetic algorithm

图8 仿真场景设置

Fig.8 Simulation scenario settings

图9 目标数量不同时的扫描捕获效率

Fig.9 Scanning capture efficiency of different scanning targets

表1 20次重复实验中贪心遗传算法迭代的最大捕获效率以及对应的遗传代数(遗传代数为100)

Table 1 Material optimal capture efficiency and iteration number during 20 iterations of the greedy genetic algorithm (Genetic algebra=100)

捕获效率和对应的遗传代数	序号
捕获效率和对应的遗传代数	1	2	3	4	5	6	7	8	9	10	11
遗传30代时捕获效率	0.8315	0.8344	0.8313	0.8337	0.8344	0.8344	0.8326	0.8344	0.8311	0.8315	0.8344
最大捕获效率	0.8344	0.8344	0.8344	0.8337	0.8344	0.8344	0.8344	0.8344	0.8344	0.8341	0.8344
最大捕获效率的遗传代数	84	8	94	27	5	19	63	6	80	92	9
捕获效率和对应的遗传代数	序号
捕获效率和对应的遗传代数	12	13	14	15	16	17	18	19	20	平均值
遗传30代时捕获效率	0.8319	0.8344	0.8344	0.8344	0.8331	0.8344	0.8337	0.8341	0.8344	0.8334
最大捕获效率	0.8344	0.8344	0.8344	0.8344	0.8344	0.8344	0.8337	0.8344	0.8344	0.8343
最大捕获效率的遗传代数	49	11	16	12	60	14	29	63	24

图10 贪心遗传算法迭代曲线

Fig.10 Iteration curve of greedy genetic algorithm

图11 不同光斑半径时的扫描捕获效率

Fig.11 Scanning capture efficiency at different spot radii

图12 不同目标移动速度时的扫描捕获效率

Fig.12 Scanning capture efficiency at different target speeds

图13 多光束扫描系统实验

Fig.13 Multi-beam scanning system experiment

图14 扫描目标与光斑显示

Fig.14 Scanned targets and spot display

图15 不同目标数量时的实际捕获效率

Fig.15 Experiment capture efficiency of different targets

图16 不同光斑半径时的实际捕获效率

Fig.16 Experiment capture efficiency at different spot radii

图17 不同目标移动速度时的扫描捕获效率

Fig.17 Experiment capture efficiency at different target speeds

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