Multi-beam Scanning of Liquid Crystal Optical Phased Array Based on Greedy Algorithm

doi:10.12382/bgxb.2022.1163

Abstract

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

CLC Number:

TN249

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.

Figures/Tables 18

Fig.1 Front view of liquid crystal polarization grating

Fig.2 Diagram of beam deflection

Fig.3 Combined structure of LCHWP and LCPG

Fig.4 Multi-beam generation system

Fig.5 16×16 scan light spots

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

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

Fig.8 Simulation scenario settings

Fig.9 Scanning capture efficiency of different scanning targets

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

Fig.10 Iteration curve of greedy genetic algorithm

Fig.11 Scanning capture efficiency at different spot radii

Fig.12 Scanning capture efficiency at different target speeds

Fig.13 Multi-beam scanning system experiment

Fig.14 Scanned targets and spot display

Fig.15 Experiment capture efficiency of different targets

Fig.16 Experiment capture efficiency at different spot radii

Fig.17 Experiment capture efficiency at different target speeds

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