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研究生:顏泓瑋
研究生(外文):YAN, HONG-WEI
論文名稱:深度學習於陣列天線場型合成之研究
論文名稱(外文):Array Antenna Pattern Synthesis Using Deep Learning Technique
指導教授:莊明霖
指導教授(外文):CHUANG MING-LIN
口試委員:李佩君莊明霖吳明典
口試委員(外文):LEE, PEI-JUNCHUANG MING-LINWU,MING-TIEN
口試日期:2022-07-22
學位類別:碩士
校院名稱:國立澎湖科技大學
系所名稱:電機工程系電資碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:73
中文關鍵詞:天線深度學習類神經網路輻射方向圖
外文關鍵詞:antennadeep learningneural networkradiation patterns
相關次數:
  • 被引用被引用:0
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這幾十年來,深度學習已在圖像辨識、自然語言處理、電腦視覺、甚至是日常實際應用取得重大的突破。深度學習是機器學習的分支,是以人工神經網路為架構對資料的特徵進行學習並從中產生出有著相應功能的數學模型,近幾年深度神經網路(deep neutral network,DNN)才被用以解偏微分方程式(Partial Differential Equations)的問題。
陣列天線可以通過改變元件、間距、相位、饋入點等來產生不同的輻射場型,其中不同相位所產生得輻射場型具有低複雜度的特性正適合用深度學習的方式訓練,這邊提出用深度神經網路用於天線輻射場型合成。陣列天線的輻射方向圖(radiation patterns)作為輸入,振幅以及相位作為輸出,並模擬出不可能從陣列天線資料內產生的輻射場型或者是不存在於訓練資料內的相位振幅資料等,並證實其可行性。

Over the decades, deep learning has made major breakthroughs in image recognition, natural language processing, computer vision, and even everyday practical applications. Deep learning is a branch of machine learning, which is based on artificial neural networks as the architecture to learn the characteristics of data and produce mathematical models with corresponding functions, in recent years, deep neutral networks (DNN) have been used to solve the problem of Partial Differential Equations.
Array antennas can be changed by changing the components, spacing, phase, feed point, etc. to produce different radiation field types, of which the radiation field types generated by different phases have low complexity characteristics that are suitable for training with deep learning, and this side proposes to use deep neural networks for antenna radiation field type synthesis. The radiation patterns of the array antennas are used as inputs, amplitudes and phases as outputs, and the radiation field types that cannot be generated from the array antenna data or the phase amplitude data that are not present in the training data are simulated and its feasibility is confirmed.
摘要 IV
致謝 V
目錄 VI
圖次 VIII
表次 XI
第一章 緒論 10
1-1陣列天線場型合成 10
1-2 深度學習 11
1-3深度學習在天線應用 12
1-4論文架構 12
第二章 陣列天線型式與預處理 14
2-1天線樣式 14
2-2資料結構 15
第三章 深度學習 17
3-1深度學習架構 17
3-2硬體設備 20
3-3類神經網路架構 20
3-4線性回歸 27
第四章 參數分析 28
4-1訓練權重 28
4-2陣列天線合成(P-) 32
4-3陣列天線合成 (A/P-) 42
4-4陣列天線合成 (R/I) 49
4-5陣列天線合成總比較 57
4-6陣列天線合成(P-)改善 60
第五章 結論 64
參考文獻 65

[1]Y.-L. Chang, Y.-C. Jiao, L. Zhang, G. Chen, and X. Qiu, “A K-band series-fed microstip array antenna with low sidelobe for anticollision radar application,” 6th Asia-Pacific Conf. on Antennas and Propag., Xi’an, China, Oct. 2017, pp. 1-3.
[2]V. K. Singh, M, Singh, A, Kumar, and A. K. Shukla, “Design of K-Band printed array antenna for SATCOM applications,” Inter. Conf. on Recent Advances in Microwave Theory and Applications, Jaipur, Rajasthan, India, Nov. 2008, pp. 702-704.
[3]S.-H. Hsu, Y.-J. Ren, and K. Chang, “A dual-polarized planar-array antenna for S-band and X-band airborne applications,” IEEE Antennas and Propag. Mag., vol. 51, no. 4, pp. 70-78, Aug. 2009.
[4]S. A. Schelkunoff, “A mathematical theory of linear arrays,” Bell System Technical Journal, vol. 22, pp. 80–107, 1943.
[5]H. G. Booker and P. C. Clemmow, “The concept of an angular spectrum of plane waves, and its relation to that of polar diagram and aperture distribution,” Proc. IEE No. 922, Radio Section, vol. 97, pt. III, pp. 11-17, Jan. 1950.
[6]P. M. Woodward, “A method for calculating the field over a plane aperture required to produce a given polar diagram,” J. IEE, vol. 93, pt. III A, pp. 1554–1558, 1946.
[7]P. M. Woodward and J. D. Lawson, “The theoretical precision with which an arbitrary radiation-pattern may be obtained from a source of a finite size,” J. IEE, vol. 95, pt. III, No. 37, pp. 363–370, Sep. 1948.
[8]D. A. McNamara, “Direct synthesis of optimum difference pattern for discrete linear arrays using Zolotarev distributions,” IET Microwaves, Antennas & Propagation, vol. 140, no.6, pp. 495-500, 1993.
[9]C. L. Dolph, “A current distribution for broadside arrays which optimizes the relationship between beam width and side-lobe level,” Proc. IRE, vol. 34, no. 6, pp.335-348, June 1946.
[10]T. T. Taylor, “Design of line source antennas for narrow beamwidth and low sidelobes,” IEEE Trans. Antennas Propag., vol. 3, no. 1, pp. 16-28, Jan. 1955.
[11]T. T. Taylor, “Design of circular apertures for narrow beamwidth and low sidelobes,” IEEE Trans. Antennas Propag., vol. 8, no. 1, pp. 17-22, Jan. 1960.
[12]R. Elliott, “Design of line source antennas for narrow beamwidth and asymmetric low sidelobes,” IEEE Trans. Antennas Propag., vol. 23, no. 1, pp. 100-107, Jan. 1975.
[13]E. T. Bayliss, “Design of monopulse antenna difference patterns with low sidelobes,” Bell Syst. Tech, J., vol. 47, pp. 632-640, 1968.
[14]A Massa, D. Marcantonio, X. Chen, M. Li, and M. Salucci, “DNNs as applied to electromagnetics, antenna, and propagation-a review,” IEEE Trans. Antennas Propag., vol. 66, no. 12, pp. 7315–7327, Dec. 2018.
[15]Y. Kim, “Application of machine learning to antenna design and radar signal processing: a review,” 2018 Int. Symp. on Antennas and Propagation, Busan, Korea, Oct. 23-26, 2018, pp. 1-2.
[16]Erricolo, P.-Y. Chen, A. Rozhkova, E. Torabi, H. Bagci, A. Shamin, and X. Zhang, “Machine learning in electromagnetics: a review and some perspectives for future research,” 2019 Int. Conf. on Electromagnetics in Advanced Applications, Granada, Spain, Sep. 9-13, 2019, pp. 1377-1380.
[17]H. M. E. Misilnani and T. Naous, “Machine learning in antenna design an overview on machine learning concept and algorithms,” 2019 Int. Conf. on High Performance Computing & Simulation, Dublin, Ireland, Jul. 15-19, 2019, pp. 600-607.
[18]H. M.Yao and L. J. Jiang, “Machine learning based neural network solving methods for the FDTD method,” in Proc. IEEE-APS, Boston, MA, USA, Jul. 2018, pp. 2321–2322.
[19]T. Shan et al., “Study on a 3D Poisson’s equation solver based on deep learning technique,” Int. Conf. Computational Electromagnatics, Chengdu, China, Mar. 26-28, 2018, pp. 1–3.
[20]F. Xu and S. Fu, “Modeling EM problem with deep neural networks,” Int. Conf. Computational Electromagnatics, Chengdu, China, Mar. 26-28, 2018, pp. 1–2.
[21]M. Singhal and G. Saini, “Optimization of antenna parameters using artificial neural network: a review,” Int. Journal of Computer Trends and Tech., Vol. 44, no. 2, pp. 64-73, Feb. 2017.
[22]Patnaik, B. Choudhury, P. Pradhan, R. Mishra and C. Christodoulou, "An ANN application for fault finding in antenna arrays," IEEE Trans. on Antennas and Propagation, vol. 55, no. 3, pp. 775-777, Mar. 2007.
[23]J. Xiao, Z. Liu, P. Zhao, Y. Li, and J. Huo, “Deep learning image reconstruction simulation for electromagnetic tomography,” IEEE Sensors J., vol. 18, no. 8, pp. 3290–3298, Apr. 2018.
[24]L. Li, L. G.Wang, F. L. Teixeira, C. Liu, A. Nehorai, and T. J. Cui, “Deep-NIS: Deep neural network for nonlinear electromagnetic inverse scattering,” IEEE Trans. Antennas Propag., vol. 67, no. 3, pp. 1819–1825, Mar. 2019.
[25]R. Guo et al., “Supervised descent learning technique for two dimensional microwave imaging,” IEEE Trans. Antennas Propag., vol. 67, no. 5, pp. 3550–3554, May 2019,
[26]H. M. Yao, M. Li, and L. Jiang, “Applying deep learning approach to the far-field subwavelength imaging based on near-field resonant metalens at microwave frequencies,” IEEE Access, vol. 7, pp. 63801-63808, May 2019.
[27]Q. Song and F. Xu, “Zero-shot learning of SAR target feature space with deep generative neural networks,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 12, pp. 2245–2249, Dec. 2017.
[28]T. Song, L. Kuang, L. Han, Y. Wang, and Q. H. Liu, “Inversion of rough surface parameters from SAR images using simulation-trained convolutional neural networks,” IEEE Geosci. Remote Sens. Lett., vol. 15, no. 7, pp. 1130–1134, Jul. 2018.
[29]J. Sonoda and T. Kimoto, “Object identification form GPR images by deep learning,” Asia-Pac. Microw. Conf., Kyoto, Japan, 2018, pp. 1298–1300.
[30]M. Alzahed, Y. M. M. antar, and S. M. Mikki, “Electromagnetic deep learning technology for radar target identification,” 2019 Int. Symp. on Antennas and Propagation and USNC-URSI Radio Sci. Meeting, Atlanta, Georgia, USA, Jul. 7-12, 2019, pp. 579-580.
[31]R. Lovato and X. Gong, “Phased antenna array beamforming using convolutional neural networks,” 2019 Int. Symp. on Antennas and Propagation and USNC-URSI Radio Sci. Meeting, Atlanta, Georgia, USA, Jul. 7-12, 2019, pp. 1247-1248.
[32]J. H. Kim and S. W. Choi, “A deep learning-based approach for radiation pattern synthesis of an array antenna,” IEEE Access, vol. 8, pp. 226059-226063, Dec. 2020.
[33]H. Huang, J. Yang, H. Huang, Y. Song, and G. Gui, “Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system,” IEEE Trans. Veh. Technol., vol. 67, no. 9, pp. 8549–8560, Sep. 2018.
[34]J. Famoriji, O. Y. Ogundepo, X. Qi, “An intelligent deep learning-based direction-of-arrival estimation scheme using spherical antenna array with unknown mutual coupling,” IEEE Access, vol. 8, pp. 179259-179271, Sep. 2020.
[35]Alkhateeb, S. Alex, P. Varkey, Y. Li, Q. Qu, and D. Tujkovic, “Deep learning coordinated beamforming for highly-mobile millimeterwave systems,” IEEE Access, vol. 6, pp. 37328–37348, 2018.
[36]H. Huang, W. Xia, J. Xiong, J. Yang, G. Zheng, and X. Zhu, “Unsupervised learning-based fast beamforming design for downlink MIMO,” IEEE Access, vol. 7, pp. 7599-7605, Dec. 2019.
[37]M. Elbir and K. V. Mishra, “Deep learning design for joint antenna selection and hybrid beamforming in massive MIMO,” 2019 Int. Symp. on Antennas and Propagation and USNC-URSI Radio Sci. Meeting, Atlanta, Georgia, USA, Jul. 7-12, 2019, pp. 1585-1586.
[38]M. Elbir and K. V. Mishra, “Joint antenna selection and hybrid beamformer design using unquantized and quantized deep learning networks,” IEEE Trans. Wireless Comm., vol. 19, no. 3, pp. 1677-1688, Mar. 2020.
[39]L. Sung and D.-H Cho, “Multi-user hybrid beamforming system based on deep neural network in millimeter-wave communication,” IEEE Access, vol. 7, pp. 791616-91625, May 2020.
[40]H. M. E. Misilmani and T. Naous, "Machine Learning in Antenna Design: An Overview on Machine Learning Concept and Algorithms," 2019 International Conference on High Performance Computing & Simulation (HPCS), 2019, pp. 600-607, doi: 10.1109/HPCS48598.2019.9188224.
[41]https://chtseng.wordpress.com/2017/09/12/%E5%88%9D%E6%8E%A2%E5%8D%B7%E7%A9%8D%E7%A5%9E%E7%B6%93%E7%B6%B2%E8%B7%AF/ 初探卷積神經網路 By CH.Tseng
[42]Facundo Bre, Juan M. Gimenez, Víctor D. Fachinotti,Prediction of wind pressure coefficients on building surfaces using artificial neural networks, Energy and Buildings, Volume 158, 2018, Pages.1429-1441,ISSN0378-7788, https://doi.org/10.1016/j.enbuild.2017.11.045.
[43]J. Tak, A. Kantemur, Y. Sharma and H. Xin, "A 3-D-Printed W-Band Slotted Waveguide Array Antenna Optimized Using Machine Learning," in IEEE Antennas and Wireless Propagation Letters, vol. 17, no. 11, pp. 2008-2012, Nov. 2018, doi: 10.1109/LAWP.2018.2857807.
[44]Güneş, Filiz. (2008).Support Vector Characterisation of the Microstrip Antennas Based on Measurements. Progress in Electromagnetics.Research.B.5.49-61.10.2528/PIERB08013006.
[45]R. Zhang et al., "Imaging Hydraulic Fractures Under Energized Steel Casing by Convolutional Neural Networks," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 12, pp. 8831-8839, Dec. 2020, doi: 10.1109/TGRS.2020.2991011.
[46]J. Bang and J. H. Kim, "Predicting Power Density of Array Antenna in mmWave Applications With Deep Learning," in IEEE Access, vol. 9, pp. 111030-111038, 2021, doi: 10.1109/ACCESS.2021.3102825.

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