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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李枝宏 | zh_TW |
| dc.contributor.advisor | Ju-Hong Lee | en |
| dc.contributor.author | 陳彥邦 | zh_TW |
| dc.contributor.author | Yen-Bang Chen | en |
| dc.date.accessioned | 2024-01-28T16:19:39Z | - |
| dc.date.available | 2024-01-29 | - |
| dc.date.copyright | 2024-01-27 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-24 | - |
| dc.identifier.citation | [1] Yih-Min Chen. On spatial smoothing for two-dimensional direction-of-arrival estimation of coherent signals. IEEE Transactions on Signal Processing, 45(7):1689–1696, 1997.
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In 2013 International Workshop on Microwave and Millimeter Wave Circuits and System Technology, pages 86–89, 2013. [17] Y.H. Sng and Youming Li. Fast algorithm for gain and phase error calibration of linear equi-spaced (les) array. In WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000, volume 1, pages 441–444 vol.1, 2000. [18] Tiedan Wang, Hongshu Liao, Liping Li, and Xu Tang. Fast doa estimation with ula in the presence of sensor gain and phase errors. In 2009 International Conference on Communications, Circuits and Systems, pages 395–397, 2009. [19] Jianfei Liu, Xiongbin Wu, William J. Emery, Lan Zhang, Chuan Li, and Ketao Ma. Direction-of-arrival estimation and sensor array error calibration based on blind signal separation. IEEE Signal Processing Letters, 24(1):7–11, 2017. [20] Zheng Dai, Weimin Su, and Hong Gu. Gain-phase errors calibration for a linear array based on blind signal separation. Sensors, 20(15), 2020. [21] Wen-Qin Wang. Virtual antenna array analysis for mimo synthetic aperture radars. International Journal of Antennas and Propagation, 2012:10, 2012. [22] Gui-mei Zheng, Baixiao Chen, and Minglei Yang. Unitary esprit algorithm for bistatic mimo radar. Electronics Letters, 48:179–181, 02 2012. [23] C. Duofang, C. Baixiao, and Q. Guodong. Angle estimation using esprit in mimo radar. Electronics Letters, 44:770 – 771, 02 2008. [24] 李坤哲Kun-Che Lee. 考慮實際環境的波束成型技術用於傳統陣列天線及多輸出多輸入雷達系統. 2018. [25] Ting Wang, Bo Ai, Ruisi He, and Zhangdui Zhong. Two-dimension direction-ofarrival estimation for massive mimo systems. IEEE Access, 3:2122–2128, 2015. [26] Ming Zhou, Xiaofei Zhang, Xiaofeng Qiu, and Chenghua Wang. Two-dimensional doa estimation for uniform rectangular array using reduced-dimension propagator method. International Journal of Antennas and Propagation, 2015:1–10, 05 2015. [27] Feng-Gang Yan, Zhi-Kun Chen, Ming-Jian Sun, Yi Shen, and Ming Jin. Two-dimensional direction-of-arrivals estimation based on one-dimensional search using rank deficiency principle. International Journal of Antennas and Propagation, 2015, 01 2015. [28] Fang-Jiong Chen, Sam Kwong, and Chi-Wah Kok. Esprit-like two-dimensional doa estimation for coherent signals. IEEE Transactions on Aerospace and Electronic Systems, 46(3):1477–1484, 2010. [29] Fang-Ming Han and Xian-Da Zhang. An esprit-like algorithm for coherent doa estimation. IEEE Antennas and Wireless Propagation Letters, 4:443–446, 2005. [30] Zhongfu Ye and Chao Liu. 2-d doa estimation in the presence of mutual coupling. IEEE Transactions on Antennas and Propagation, 56(10):3150–3158, 2008. [31] Han Wu, Chunping Hou, Hua Chen, Wei Liu, and Qing Wang. Direction finding and mutual coupling estimation for uniform rectangular arrays. Signal Processing, 117:61–68, 2015. [32] Xiaofei Zhang, Lingyun Xu, Lei Xu, and Dazhuan Xu. Direction of departure (dod) and direction of arrival (doa) estimation in mimo radar with reduced-dimension music. IEEE Communications Letters, 14(12):1161–1163, 2010. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91511 | - |
| dc.description.abstract | 本篇碩士論文主要探討如何在2-D URA天線陣列下進行雷達天線陣列的角度估計，會將問題延伸到雙基地台多重輸入多重輸出(Bisatic MIMO)上，並解決多重誤差對角度估計造成的影響。多重誤差包括未知天線耦合誤差、同調訊號環境、天線耦合誤差+同調訊號環境。
相較於使用一維均勻天線陣列，因為二維天線陣列能夠偵測物體的仰角與方位角，能更精確估測出物體在空間中的位置，因此決定將二維天線陣列當作本論文的架構之一。 本論文前段將會討論EPSRIT角度估計演算法(Estimation of Signal Parameters via Rotational Invariant Techniques)。最後，多重非理想環境將會加入討論，並解決這些非理想環境對角度估計在2-D URA下的影響。 為了解決上述的多重非理想環境，首先使用依據未知天線耦合距離與天線陣列大小所產生的選取矩陣，解決未知天線耦合對2-D URA角度估計所帶來的影響。再者，利用空間平滑(Spatial Smoothing)與共變異數矩陣重建(Covariance Matrix Reconstruction)解決同調訊號環境(Coherent Signals)對2-D URA角度估計所帶來的影響。最後，將會探討如何在誤差環境為天線耦合與同調訊號雙重誤差環境下，解決誤差環境對2-D URA角度估計帶來的誤差。 | zh_TW |
| dc.description.abstract | The goal of this thesis is to explore the angle estimation problems of arrivals for Bistatic MIMO radar with two-dimensional (2-D) uniform rectangular array (URA) deployed at its transmitter and receiver. The proposed method deals with multiple mismatches, including mutual coupling between array sensors, coherent signals, and mutual coupling plus coherent signals. Compared to one-dimensional uniform linear arrays (1-D ULAs), 2-D URAs have the advantages of estimating the azimuth and elevation of targets simultaneously and producing a more precise estimation. Due to the higher complexity required by employing the conventional MUSIC, we utilize the ESPRIT algorithm for finding the angles of the targets in the presence of Coherent signals, mutual coupling, and coherent signals plus mutual coupling. To deal with the multiple mismatches mentioned above, we present several specific selection operations working on the signal subspace of the received array data in conjunction with spatial smoothing scheme and covariance matrix reconstruction. Computer simulation results are provided for confirmation and comparison. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-01-28T16:19:39Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-01-28T16:19:39Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書i
致謝iii 摘要v Abstract vii 目錄ix 第一章緒論1 1.1 研究背景與動機. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 研究貢獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 第二章一維天線陣列雷達下的訊號陣列模型及偵測DOA 角度之方式5 2.1 在天線陣列擺列方式為ULA(Uniform Linear Array) 下的訊號模型. 5 2.1.1 共變異數矩陣(Covariance Matrix) . . . . . . . . . . . . . . . . . 6 2.2 ESPRIT 演算法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 第三章非理想環境11 3.1 同調訊號(Coherent Signal) . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.1 Spatial Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1.1.1 Comparison . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.1.2 My method . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2 未知天線耦合(Unknown Mutual Coupling) . . . . . . . . . . . . . . . 17 3.2.1 訊號模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.2 使用ESPRIT 對抗未知天線耦合的影響. . . . . . . . . . . . . . 18 3.2.3 未知天線耦合係數的估測. . . . . . . . . . . . . . . . . . . . . . 19 3.2.3.1 Result . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 增益與相位誤差(Gain & Phase error) . . . . . . . . . . . . . . . . . . 24 3.3.1 相位誤差(Phase Error) . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.1.1 訊號模型. . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.1.2 估測相位誤差矩陣. . . . . . . . . . . . . . . . . . . 24 3.3.2 增益誤差(Gain Error) . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3.2.1 訊號模型. . . . . . . . . . . . . . . . . . . . . . . . 27 3.3.2.2 估測增益誤差矩陣. . . . . . . . . . . . . . . . . . . 27 3.3.2.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . 28 第四章在Bistatic MIMO 一維天線陣列雷達下的訊號模型及角度估測演算法31 4.1 訊號模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.2 運用ESPRIT 在Bistatic MIMO 下進行角度估測. . . . . . . . . . . 32 4.3 配對DOD 與DOA . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 第五章在Bistatic MIMO 一維天線陣列雷達下探討非理想環境之問題37 5.1 同調訊號(Coherent Signal) . . . . . . . . . . . . . . . . . . . . . . . 37 5.1.1 VWS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.1.2 與第3.1.1 章中的作法結合. . . . . . . . . . . . . . . . . . . . . 39 5.1.3 5.1.2 模擬結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.1.4 運用座標圖觀察DOD 與DOA 估測表現. . . . . . . . . . . . . 42 5.2 未知天線耦合(Unknown Mutual Coupling) . . . . . . . . . . . . . . . 44 5.2.1 訊號模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5.2.2 解決方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2.3 模擬結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 第六章在2-D URA 天線陣列雷達下的訊號模型及角度估測演算法49 6.1 訊號模型Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . 49 6.2 使用ESPRIT 演算法進行DOA 方位角與仰角的估計. . . . . . . . 52 6.3 在相同數量之URA 天線個數下改變x 軸與y 軸的天線個數並觀察方位角與仰角RMSE 的變化. . . . . . . . . . . . . . . . . . . . . . 54 6.3.1 比較一. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 6.3.2 比較二. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.3.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 第七章2D-URA 天線陣列雷達在誤差環境下進行方位角與仰角估測61 7.1 在同調訊號下進行方位角與仰角估測. . . . . . . . . . . . . . . . . 61 7.1.1 Spatial Smoothing [1, 2] . . . . . . . . . . . . . . . . . . . . . . . 61 7.1.2 共變異數矩陣重建(Covariance Matrix Reconstruction) . . . . . . 63 7.1.3 在2-D URA 天線數為偶數時進行共變異數矩陣重建. . . . . . . 67 7.1.3.1 模擬. . . . . . . . . . . . . . . . . . . . . . . . . . . 70 7.1.4 比較SSP 與CMR . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7.2 在非理想環境為未知天線耦合下估測DOA 方位角與仰角. . . . . . 79 7.2.1 受到先線耦合影響的2-D URA 訊號模型. . . . . . . . . . . . . 79 7.2.2 使用選取矩陣選取訊號子空間的子矩陣進行DOA 估計. . . . . 81 7.2.3 使用中間矩陣忽略天線耦合的影響並進行DOA 估測. . . . . . 83 第八章在Bisataic MIMO 2-D URA 天線陣列雷大下的訊號模型並使用ESPRIT 演算法進行角度估測︒ 87 8.1 訊號矩陣的向量化. . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 8.2 使用ESPRIT 演算法進行DOD 與DOA 角度估計. . . . . . . . . . 91 第九章Bistatic MIMO 2-D URA 天線陣列雷達在誤差環境下進行角度估測97 9.1 Bistatic MIMO 2-D URA 在非理想環境為同調訊號下使用ESPRIT進行角度估測. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 9.1.1 共變異數矩陣重建. . . . . . . . . . . . . . . . . . . . . . . . . . 97 9.1.2 模擬比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 9.2 Bistatic MIMO 2-D URA 在非理想環境為未知天線耦合下使用ESPRIT 進行角度估測. . . . . . . . . . . . . . . . . . . . . . . . . . 106 9.2.1 使用選取矩陣選取訊號子空間進行ESPRIT DOA 估測. . . . . 110 9.2.2 Middle Array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 9.2.3 模擬. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 9.3 Bistatic MIMO 2-D URA 在非理想環境為同調環境與未知天線耦合下利用ESPRIT 進行角度估測. . . . . . . . . . . . . . . . . . . . . 116 9.3.1 訊號模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 9.3.2 模擬. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 第十章結論及未來展望123 參考文獻125 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 雙基地台多重輸入多重輸出 | zh_TW |
| dc.subject | 角度估計 | zh_TW |
| dc.subject | ESPRIT 演算法 | zh_TW |
| dc.subject | 未知天線耦合 | zh_TW |
| dc.subject | 同調訊號環境誤差 | zh_TW |
| dc.subject | 空間平滑 | zh_TW |
| dc.subject | 共變異數矩陣重建 | zh_TW |
| dc.subject | Covariance Matrix Reconstruction | en |
| dc.subject | Bistatic MIMO Radar | en |
| dc.subject | Estimation of Arrival | en |
| dc.subject | ESPRIT | en |
| dc.subject | Unknown Mutual Coupling | en |
| dc.subject | Coherent Signals | en |
| dc.subject | Spatial Smoothing | en |
| dc.title | 在二維天線陣列雙基地台多輸入多輸出架構下使用 ESPRIT 與共變異數矩陣重建進行目標物角度估計 | zh_TW |
| dc.title | Bistatic MIMO Estimation of Arrival with 2-D URA under Multiple Mismatches using ESPRIT based on Covariance Matrix Reconstruction | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 劉俊麟;方文賢 | zh_TW |
| dc.contributor.oralexamcommittee | Chun-Lin Liu;Wen-Hsien Fang | en |
| dc.subject.keyword | 雙基地台多重輸入多重輸出,角度估計,ESPRIT 演算法,未知天線耦合,同調訊號環境誤差,空間平滑,共變異數矩陣重建, | zh_TW |
| dc.subject.keyword | Bistatic MIMO Radar,Estimation of Arrival,ESPRIT,Unknown Mutual Coupling,Coherent Signals,Spatial Smoothing,Covariance Matrix Reconstruction, | en |
| dc.relation.page | 128 | - |
| dc.identifier.doi | 10.6342/NTU202301895 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-07-26 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 電信工程學研究所 | - |
| dc.date.embargo-lift | 2028-07-21 | - |
| 顯示於系所單位： | 電信工程學研究所 | |
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