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Optical fiber phased array can be used in high-power laser beam combination, lidar and other areas. The configuration of the optical fiber array is different from the microwave phased array, which has periodic problems that affect the energy intensity distribution of the main lobe. Starting from the physical model, in this paper we establish a theoretical model of optical phased array antenna array based on a set of concentric circular ring lattices, and propose a theory of the rapid synthesis of randomly configured interference field strengths through using analytical continuation method and Fourier transform method. The problem of sampling bandwidth and sampling number that should be paid attention to in the numerical simulation of discrete sampling are discussed, and the problem of quickly realizing the numerical simulation of multi-beam interference field is solved. Genetic algorithm and particle swarm algorithm for optimizing the configuration of optical phased array antennas are investigated with different populations. The convergence speeds and optimization efficiencies of the two algorithms are compared and analyzed. It is demonstrated that the peak side-lobe ratio PSR can be achieved to be better than 0.270 by the genetic algorithm optimized configuration array under the real fabricate parameter. The proposed method is expected to be used in the actual optical phased array antenna configuration to guide the optimal design of the antenna with low side lobes, and the proposed model is also expected to provide a certain reference value for the study of optimizing the non-differentiable objective function.
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Keywords:
- optical fiber phased array /
- lidar /
- genetic algorithm /
- particle swarm algorithm
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
Li M F, Yan L, Yang R, Liu Y X 2019 Acta Phys. Sin. 68 064202
Google Scholar
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Google Scholar
[10] 李明飞, 袁梓豪, 赵琳琳, 孙晓洁 2020 导航与控制 19 40
Li M F, Yuan Z H, Zhao L L, Sun X J 2020 19 40 (in Chinese)
[11] Mandal D, Ghoshal S P, Bhattacharjee A K 2011 J. Netw. Comput. Appl. 1 94
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Google Scholar
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Google Scholar
[14] Reyna A, Panduro M A 2008 J. Electromagnet. Wave. 22 2241
Google Scholar
[15] Chen K S, Zhu Y Y, Ni X L, Chen H 2016 Int. J. Antenn. Propag. 2015 147247
[16] Reyna A, Panduro M A, Bocio D 2014 EuCAP. IEEE. 2014 1513
[17] Zhang D, Zhang F, Pan S 2018 Opt. Commun. 419 47
Google Scholar
[18] 王玲玲, 方大纲 2003 电子学报 31 2135
Google Scholar
Wang L L, Fang D G 2003 Acta Electronica Sin. 31 2135
Google Scholar
[19] Ji X, Pu Z, Jia X 2009 Opt. Commun. 282 2685
Google Scholar
[20] Liu C B, Lu F, Wu C, Han X E 2015 Opt. Commun. 346 26
Google Scholar
[21] Li J C, Yuan C J, Tankam P, Picart P 2011 Opt. Commun. 284 3202
Google Scholar
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图 1 光纤阵列及远场分布示意图
Figure 1. Diagrammatic sketch of the fiber array and its far-field intensity.
图 2 光纤的配置集合与单环排布方式
Figure 2. Fiber configuration set and single ring configuration.
图 3 光纤单环排布产生的光场强度分布
Figure 3. Far-field intensity distribution of the single ring configuration.
图 4 遗传算法的工作流程
Figure 4. Flow chart of the genetic algorithm.
图 5 种群数分别为
${N_{{\rm{pop}}}} = $ 10, 20, 50时遗传算法的优化结果Figure 5. Genetic algorithm optimized results with populations
${N_{{\rm{pop}}}} = $ 10, 20, 50.
图 6 粒子群算法工作流程图
Figure 6. Flow chart of the particle swarm optimization.
图 7 种群数分别为
${N_{{\rm{pop}}}} = $ 10, 20, 50时粒子群算法的优化结果Figure 7. Particle swarm optimization algorithm results with populations
${N_{{\rm{pop}}}} = $ 10, 20, 50.
图 8 优化前光场强度分布与遗传算法优化后光场强度分布,
${N_{{\rm{pop}}}} = $ 20Figure 8. Far field intensity distribution before and after the genetic algorithm optimized results with the population
${N_{{\rm{pop}}}} = $ 20. -
[1] Ibarra M, Panduro M A, Andrade Á G, Reyna A 2015 J. Electromagnet. Wave. 29 1983
Google Scholar
[2] David K, Kermène V, Marc F, Joel B, Alain B 2017 Opt. Express. 25 13816
Google Scholar
[3] Zhang F Z, Zhang D C, Pan S L 2018 Appl. Opt. 57 4977
Google Scholar
[4] Yin S, Kim J H, Wu F, Ruffin P, Luo C 2007 Opt. Commun. 270 41
Google Scholar
[5] Montoya J, Sanchez-Rubio A, Hatch R, Payson H 2014 Appl. Opt. 53 7551
Google Scholar
[6] Wu H, Wang C L, Gong W L 2018 Opt. Express 26 4183
Google Scholar
[7] 王凯, 黎全, 林惠祖, 孙帅, 高少博 2016 光学学报 36 0227002
Google Scholar
Wang K, Li Q, Lin H, Sun S, Gao S B 2016 Acta. Opt. Sin. 36 0227002
Google Scholar
[8] 李明飞, 阎璐, 杨然, 刘院省 2019 物理学报 68 064202
Google Scholar
Li M F, Yan L, Yang R, Liu Y X 2019 Acta Phys. Sin. 68 064202
Google Scholar
[9] Liu C B, Chen J Q, Liu J X, Han X E 2018 Opt. Express 26 10048
Google Scholar
[10] 李明飞, 袁梓豪, 赵琳琳, 孙晓洁 2020 导航与控制 19 40
Li M F, Yuan Z H, Zhao L L, Sun X J 2020 19 40 (in Chinese)
[11] Mandal D, Ghoshal S P, Bhattacharjee A K 2011 J. Netw. Comput. Appl. 1 94
[12] Bera R, Mandal D, Kar R, Ghoshal S P 2017 Comput. Electr. Eng. 61 151
Google Scholar
[13] Panduro M A, Mendez A L, Dominguez R, Romero G 2006 Aeu-int. J. Electron. C. 60 713
Google Scholar
[14] Reyna A, Panduro M A 2008 J. Electromagnet. Wave. 22 2241
Google Scholar
[15] Chen K S, Zhu Y Y, Ni X L, Chen H 2016 Int. J. Antenn. Propag. 2015 147247
[16] Reyna A, Panduro M A, Bocio D 2014 EuCAP. IEEE. 2014 1513
[17] Zhang D, Zhang F, Pan S 2018 Opt. Commun. 419 47
Google Scholar
[18] 王玲玲, 方大纲 2003 电子学报 31 2135
Google Scholar
Wang L L, Fang D G 2003 Acta Electronica Sin. 31 2135
Google Scholar
[19] Ji X, Pu Z, Jia X 2009 Opt. Commun. 282 2685
Google Scholar
[20] Liu C B, Lu F, Wu C, Han X E 2015 Opt. Commun. 346 26
Google Scholar
[21] Li J C, Yuan C J, Tankam P, Picart P 2011 Opt. Commun. 284 3202
Google Scholar
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