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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電信工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85147
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dc.contributor.advisor李枝宏(Ju-Hong Lee)
dc.contributor.authorZong-Ying Yeen
dc.contributor.author葉宗穎zh_TW
dc.date.accessioned2023-03-19T22:46:31Z-
dc.date.copyright2022-08-19
dc.date.issued2022
dc.date.submitted2022-08-10
dc.identifier.citationR. Schmidt, 'Multiple emitter location and signal parameter estimation,' IEEE Transactions on Antennas and Propagation, vol. 34, no. 3, pp. 276-280, 1986. L. Li, F. Chen, and J. Dai, 'Separate DOD and DOA Estimation for Bistatic MIMO Radar,' International Journal of Antennas and Propagation, vol. 2016, pp. 1-11, 2016. 許恩齊, '雙基地多輸入多輸出雷達在非理想環境之方位角估計,' 碩士, 電信工程學研究所, 國立臺灣大學, 台北市, 2019. P. Pal and P. P. Vaidyanathan, 'Coprime sampling and the music algorithm,' in 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE), 2011, pp. 289-294. C. L. Liu and P. P. Vaidyanathan, 'Remarks on the Spatial Smoothing Step in Coarray MUSIC,' IEEE Signal Processing Letters, vol. 22, no. 9, pp. 1438-1442, 2015. C. Liu, P. P. Vaidyanathan, and P. Pal, 'Coprime coarray interpolation for DOA estimation via nuclear norm minimization,' in 2016 IEEE International Symposium on Circuits and Systems (ISCAS), 2016, pp. 2639-2642. Y. Jia, X. Zhong, Y. Guo, and W. Huo, 'DOA and DOD estimation based on bistatic MIMO radar with co-prime array,' in 2017 IEEE Radar Conference (RadarConf), 2017, pp. 0394-0397. L. Bin and C. Shing-Chow, 'Adaptive Beamforming for Uniform Linear Arrays With Unknown Mutual Coupling,' IEEE Antennas and Wireless Propagation Letters, vol. 11, pp. 464-467, 2012. F. Wu, F. Cao, X. Feng, X. Ni, and C. Chen, 'Sparse linear array with low mutual coupling ratio for DOA estimation,' IET Communications, vol. 15, no. 6, pp. 866-873, 2021. Y. Zhang and Z. Ye, 'Efficient Method of DOA Estimation for Uncorrelated and Coherent Signals,' IEEE Antennas and Wireless Propagation Letters, vol. 7, pp. 799-802, 2008. S. Mohammadzadeh and O. Kukrer, 'Adaptive beamforming based on theoretical interference‐plus‐noise covariance and direction‐of‐arrival estimation,' IET Signal Processing, vol. 12, no. 7, pp. 819-825, 2018. 呂柏叡, '雙基地多輸入多輸出雷達在多誤差環境的存在下之角度估計,' 碩士, 電信工程學研究所, 國立臺灣大學, 台北市, 2021. J. Dai, X. Bao, N. Hu, C. Chang, and W. Xu, 'A Recursive RARE Algorithm for DOA Estimation With Unknown Mutual Coupling,' IEEE Antennas and Wireless Propagation Letters, vol. 13, pp. 1593-1596, 2014. B. Friedlander and A. J. Weiss, 'Direction finding in the presence of mutual coupling,' IEEE Transactions on Antennas and Propagation, vol. 39, no. 3, pp. 273-284, 1991. W.-q. Wang, 'Virtual Antenna Array Analysis for MIMO Synthetic Aperture Radars,' International Journal of Antennas and Propagation, vol. 2012, pp. 1-10, 2012. J.-F. Determe, J. Louveaux, L. Jacques, and F. Horlin, 'Simultaneous Orthogonal Matching Pursuit With Noise Stabilization: Theoretical Analysis,' ArXiv, 2015. J.-F. Determe, J. Louveaux, L. Jacques, and F. Horlin, 'On The Exact Recovery Condition of Simultaneous Orthogonal Matching Pursuit,' IEEE Signal Processing Letters, vol. 23, no. 1, pp. 164-168, 2016. J. A. Tropp, A. C. Gilbert, and M. J. Strauss, 'Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit,' Signal Processing, vol. 86, no. 3, pp. 572-588, 2006. M. E. Davies and Y. C. Eldar, 'Rank Awareness in Joint Sparse Recovery,' IEEE Transactions on Information Theory, vol. 58, no. 2, pp. 1135-1146, 2012. C. Zhang, H. Huang, and B. Liao, 'Direction Finding in MIMO Radar With Unknown Mutual Coupling,' IEEE Access, vol. 5, pp. 4439-4447, 2017. S. Mirjalili, S. M. Mirjalili, and A. Lewis, 'Grey Wolf Optimizer,' Advances in Engineering Software, vol. 69, pp. 46-61, 2014. A. Kaveh and P. Zakian, 'Improved GWO algorithm for optimal design of truss structures,' Engineering with Computers, vol. 34, no. 4, pp. 685-707, 2018.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85147-
dc.description.abstract本論文目標為探討雙基地(Bistatic)多輸入多輸出雷達系統的角度估計問題,包含估計訊號離開方向(direction of departure, DOD)與訊號到達方向(direction of arrival, DOA)。並且相較於傳統大多使用均勻線性陣列(Uniform Linear Array, ULA)作為傳送端與接收端天線架構,近年稀疏陣列(sparse array)天線架構為一大熱門研究方向,本論文主要想探討在雙基地多輸入多輸出雷達系統架構下,使用稀疏陣列中的互質陣列(coprime)作為傳送端與接收端天線架構估計DOD與DOA,並探討角度估計在實際情形下會受到的非理想環境,如天線耦合誤差(mutual coupling)、天線元件增益與相位誤差(gain and phase error)、同調環境(coherence)等。 為了對抗上述的誤差環境,首先我們參考文獻Separate MUSIC-IGRRARE演算法,利用Separate運算將接收資料矩陣分為傳送端與接收端的資料矩陣,並使用角度估計問題領域中的MUSIC演算法進行初始角度估計後,利用元啟發演算法(meta-heuristic)中的灰狼最佳化演算法(Grey Wolf Optimization,GWO)進行天線耦合誤差與增益與相位誤差的參數最佳化搜索,最終利用疊代方法修正受到天線耦合誤差與增益與相位誤差的估計角度。zh_TW
dc.description.abstractThis paper aims to discuss the angle estimation problem of the bistatic multiple input multiple output radar system, including estimating the signal direction of departure (DOD) and the direction of arrival (DOA). And compared with the traditional use of a uniform linear array (ULA) as the antenna structure at the transmitting and receiving end, the sparse array antenna structure has become a hot research topic in recent years. Under the multi-input multi-output radar system architecture, the coprime array of the sparse array is used as the antenna structure of the transmitting end and the receiving end to estimate DOD and DOA, and to discuss the non-ideal environment that angle estimation will be subjected to in practical situations, such as antennas coupling error (mutual coupling), antenna element gain and phase error (gain and phase error), coherent environment (coherent) and so on. To combat the above error environment, first, we refer to the separate MUSIC-IGRRARE algorithm, use the Separate operation to divide the received data matrix into the data matrix of the transmitter and the receiver, and use the MUSIC algorithm in the field of angle estimation to estimate the initial angle. Then, the Grey Wolf Optimization (GWO) in the meta-heuristic algorithm is used to optimize the parameters of the antenna coupling error, gain and phase error, and finally, the iterative method is used to correct the estimated angle of antenna coupling error and gain and phase error.en
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dc.description.tableofcontents論文口試委員審定書 # 致謝 i 中文摘要 ii ABSTRACT iii 目錄 iv Chapter1 緒論 1 1.1 研究動機與背景 1 1.2 論文貢獻 2 1.3 論文架構 2 Chapter2 相位雷達陣列之數學模型 4 2.1 線性均勻陣列之接收訊號數學模型 4 2.2 互質陣列(Coprime array)之接收訊號數學模型 5 2.3 共變異數矩陣性質(Covariance Matrix) 6 2.4 非理想環境 11 2.4.1 未知天線相互耦合(Unknown Mutual Coupling, UMC) 11 2.4.2 同調誤差(Coherent) 12 2.4.3 天線元件增益與相位誤差(Gain and Phase Error, GPE) 13 2.4.4 同調本地散射(Coherent Local Scattering, CLS) 13 Chapter3 相位雷達之角度估計法 15 3.1 Root Mean Square Error(RMSE) 15 3.2 Multiple Signal Classification(MUSIC)演算法 15 3.3 Recursive Rank-Reduction Algorithm(Recursive -RARE) 15 Chapter4 雙基地多輸入多輸出雷達系統(Bistatic MIMO Radar) 18 4.1 ULA-MIMO雷達系統之訊號模型 18 4.2 Coprime-MIMO雷達系統之訊號模型 21 4.3 MIMO雷達系統中之誤差環境 22 4.3.1 未知天線相互耦合(Unknown Mutual Coupling, UMC) 22 4.3.2 同調(Coherent) 22 4.3.3 天線元件增益與相位誤差(Gain and Phase Error, GPE) 23 4.3.4 同調本地散射(Coherent Local Scattering, CLS) 23 Chapter5 MIMO架構下之角度估計法與嘗試的方法 25 5.1 二維MUSIC 25 5.2 Separate MUSIC 25 5.3 Separate Simultaneous Orthogonal Matching Pursuit Method (Separate - SOMP) 29 5.3.1 Separate運算得到VT與VR 29 5.3.2 字典矩陣(dictionary matrix) 29 5.3.3 疊代估計DOD 30 5.3.4 疊代估計DOA 32 5.4 Separate MUSIC Iteration Gradient R-RARE(Separate MUSIC IGRRARE) 35 5.4.1 估計DOD流程 35 5.4.2 估計DOA流程 39 5.4.3 角度配對 43 5.5 Separate Grey Wolf Optimization algorithm 43 5.5.1 建立含DOD/DOA資訊之訊號矩陣 47 5.5.2 使用GWO估計DOD/DOA 49 5.6 實驗模擬 51 5.6.1 UMC+GPE+Coherence誤差環境 51 5.6.2 GPE+Coherence誤差環境 53 Chapter6 Iteration Separate MUSIC GWO(IS MUSIC GWO) 56 6.1 Iteration Separate MUSIC GWO(IS MUSIC GWO) 56 6.1.1 DOD估計的流程 57 Step 1.建立含DOD資訊之訊號矩陣VT 57 6.1.2 DOA估計的流程 61 Step 1.建立含DOA資訊之訊號矩陣 61 6.2 實驗模擬 65 6.2.1 UMC+GPE誤差環境並變動SNR 65 6.2.2 UMC+GPE誤差環境並變動天線數 67 6.2.3 UMC+GPE誤差環境並變動角度間距 69 6.2.4 UMC+GPE誤差環境並變動角度數量 71 6.2.5 GPE+Coherence誤差環境並變動SNR 73 6.2.6 GPE+Coherence誤差環境並變動天線數 75 6.2.7 GPE+Coherence誤差環境並變動角度間距 77 6.2.8 GPE+Coherence誤差環境並變動角度數量 79 6.2.9 UMC+GPE+Coherence誤差環境並變動SNR 81 6.2.10 UMC+GPE+Coherence誤差環境並變動天線數 83 6.2.11 UMC+GPE+Coherence誤差環境並變動角度間距 85 6.2.12 UMC+GPE+Coherence誤差環境並變動角度數量 87 6.3 實驗模擬二:具CLS誤差的誤差環境 88 6.3.1 CLS+GPE誤差環境並變動SNR 88 6.3.2 CLS+GPE誤差環境並變動天線數 90 6.3.3 CLS+GPE誤差環境並變動角度間距 92 6.3.4 CLS+GPE誤差環境並變動角度數量 94 6.3.5 CLS+GPE+UMC+Coherence誤差環境並變動SNR 96 6.3.6 CLS+GPE+UMC+Coherence誤差環境並變動天線數 98 6.3.7 CLS+GPE+UMC+Coherence誤差環境並變動角度間距 100 6.3.8 CLS+GPE+UMC+Coherence誤差環境並變動角度數量 102 6.4 Iteration Separate MUSIC Grey Wolf Optimization時間複雜度 105 6.4.1 CPU運算時間 106 Chapter7 結論與未來研究 108 Chapter8 參考文獻 109
dc.language.isozh-TW
dc.subject灰狼最佳化zh_TW
dc.subject雙基地多輸入多輸出雷達系統zh_TW
dc.subject互質陣列zh_TW
dc.subject天線耦合誤差zh_TW
dc.subject天線元件增益與相位誤差zh_TW
dc.subject同調環境誤差zh_TW
dc.subject分離多信號分類演算法zh_TW
dc.subject元啟發式演算法zh_TW
dc.subjectcoherenten
dc.subjectGrey Wolf Optimizationen
dc.subjectMeta-heuristicen
dc.subjectSeparate MUSICen
dc.subjectBistatic MIMO Radar Systemen
dc.subjectcoprime arrayen
dc.subjectmutual couplingen
dc.subjectgain and phase erroren
dc.title利用元啟發演算法於互質雙基地多輸入多輸出雷達在多重誤差環境下之角度估計zh_TW
dc.titleDirection Finding Based on Meta-heuristic Optimization Concept for Coprime Bistatic MIMO Radar under Multiple Mismatchesen
dc.typeThesis
dc.date.schoolyear110-2
dc.description.degree碩士
dc.contributor.oralexamcommittee劉俊麟(Chun-Lin Liu),謝宏昀(Hung-Yun Hsieh)
dc.subject.keyword雙基地多輸入多輸出雷達系統,互質陣列,天線耦合誤差,天線元件增益與相位誤差,同調環境誤差,分離多信號分類演算法,元啟發式演算法,灰狼最佳化,zh_TW
dc.subject.keywordBistatic MIMO Radar System,coprime array,mutual coupling,gain and phase error,coherent,Separate MUSIC,Meta-heuristic,Grey Wolf Optimization,en
dc.relation.page110
dc.identifier.doi10.6342/NTU202202212
dc.rights.note同意授權(限校園內公開)
dc.date.accepted2022-08-10
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept電信工程學研究所zh_TW
dc.date.embargo-lift2025-07-31-
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