
系統識別號 
U00260102201518012600 
論文名稱(中文) 
應用天線模式切換與極化分集技術於目標方向偵測及其辨識 
論文名稱(英文) 
Application of Antenna Mode Switch and Polarization Diversity to Target Direction Finding and Recognition 
校院名稱 
成功大學 
系所名稱(中) 
系統及船舶機電工程學系 
系所名稱(英) 
Department of Systems and Naval Mechatronic Engineering 
學年度 
103 
學期 
1 
出版年 
104 
研究生(中文) 
鄭世杰 
研究生(英文) 
ShihChieh Cheng 
學號 
p18951098 
學位類別 
博士 
語文別 
英文 
論文頁數 
107頁 
口試委員 
指導教授李坤洲 口試委員王健仁 口試委員潘欣泰 口試委員江景泰 口試委員何明華 口試委員張名先 口試委員卿文龍

中文關鍵字 
天線模式切換
方向估測
極化分集
目標辨識
主成份分析

英文關鍵字 
Antenna Mode Switch
DOA Estimation
Polarization Diversity
Target Recognition
Principal Component Analysis

學科別分類 

中文摘要 
本論文先以延遲總和法與最小變異無扭曲法結合天線模式切換技術來做目標來源方向的估測，等目標方向確定之後，再以天線極化分集技術結合主成份分析法來作雷達目標辨識。
本論文的第一部分主要探討以兩種方向估測方法結合天線模式切換技術應用於訊號來源的方向判斷。在方向判斷的研究中，我們以傳統簡易的延遲總和法與最小變異無扭曲法做為改善方向估測方法的測試演算法。延遲總和法是最簡單而運算快速的方向估測方法之一，結合天線模式切換的技術，可以減少天線背瓣對於天線主瓣的干擾，進而大幅提升方向估測的準確度，大幅減少需要的天線陣列的尺寸與運算所需要的矩陣大小。在實際的天線設計上，本研究提出了左右對稱式的領結天線來實現這個天線模式切換技術，量測實際的天線輻射場型並把資料輸入應用天線模式切換技術的演算法，得到預期的精準度提升與縮小天線陣列的目的，也驗證了本論文運用天線模式切換技術的實作可行性。為了提出更強力的理論基礎，本論文推導了一個針對兩個天線陣列元素運用天線模式切換技術於訊號源方向估測的全新公式，由此公式可以清楚的看出天線背瓣的大小，對於訊號源方向的估測精準度與失敗率的影響有多少，也可以算出天線陣列元素間的間隔，影響訊號源方向估測精準度與失敗率的程度。為了更進一步提升訊號源方向的估測精準度，也將天線模式切換技術結合最小變異無扭曲法，得到估測精準度、成功率、抗雜訊能力大幅度的提升，天線數目與天線彼此間距也可以因此而減少。
在估測到訊號源(即目標)的方向之後，我們希望可以進一步知道來源目標物是什麼？因此，本研究的第二部分主要探討應用極化分集雷達散射截面積(Radar Cross Section, RCS)於雷達目標辨識。我們利用電磁模擬軟體ANSYSHFSS模擬目標物在不同極化條件下的散射電磁波，再結合主成份分析(Principal Component Analysis, PCA)訊號處理，來作雷達目標辨識。模擬結果顯示本論文所提出的雷達目標辨識演算法能夠達到高辨識率，且運算雷達散射截面積所需要的矩陣大小也大幅度縮小，且驗證了即使處在高雜訊的環境中，仍然能保有高準確率。
本論文共分為七章。第一章為緒論：介紹文獻回顧、研究動機、研究貢獻及論文架構。第二章介紹本研究所牽涉到的基本理論。第三章運用延遲總和法結合天線模式切換技術來提高船艦方向估測準確率與縮減陣列大小。第四章是推導公式來精確估計與預測應用延遲總和演算法結合天線模式切換技術的訊號源方向判斷。第五章運用最小變異無扭曲演算法結合天線模式切換技術來進一步提高訊號源方向估測準確率、縮減陣列大小、並提高抗雜訊的能力。第六章利用極化分集雷達散射截面積結合主成份分析法作雷達目標辨識。第七章為本研究的結論。

英文摘要 
In this dissertation, delayandsum method, minimum variance distortionless response method, and antenna mode switch techniques are applied to target DOA (DirectionOfArrival) estimation. After the target DOA is successfully estimated, the antenna polarization diversity technique, RCS (Radar Cross Section) and PCA (Principal Component Analysis) are further applied to identify the type of a target.
In the first part of this research, two DOA estimation methods based on antenna mode switch techniques are utilized for improving the DOA estimation of incoming signals. The improvement of DOA estimation is implemented on conventional delayandsum method and minimum distortionless response, which are very simple and fast in calculation. With the use of antenna mode switch techniques, interferences from the antenna back lobe are greatly reduced and then the accuracy of DOA estimation is significantly improved. Moreover, the required size of an antenna array and its matrix calculation efforts can be decreased. To practically implement the above concept, a symmetric bowtie antenna is proposed in this study. By measuring its gain pattern and then taking the resulting data as input to the above improved DOA algorithms, the results are very good and consistent with the simulation. To give insight into the theoretical support, we derived a novel formula to interpret the improvement of DOA estimation. Our derived formula clearly shows how antenna back lobes significantly interfere the DOA estimation of incoming signals. By this formula, the influence of array element spacing and accuracy of DOA estimation can be predicted. For the purpose of better accuracy, the antenna mode switch is further combined with the minimum variance distortionless response method for DOA estimation of incoming signals. It shows that the accuracy, successful recognition rate and ability of tolerating noise are significantly improved. With the use of such techniques, the required array element number and element spacing are decreased.
After the DOA of an incoming signal is successfully estimated, it further motivates us to identify the type of signal source, i.e., to recognize the target. In the second part of this research, the radar target recognition is given based on polarizationdiversity RCS and PCA signal processing. The polarizationdiversity of this study means changing the polarizations of incident waves and antennas. Without loss of generality, models of ships are used as targets of recognition for simplicity. The RCS is simulated by ANSYSHFSS software. For consideration of better discrimination, noisereduction and easy computation, the identification is implemented on PCA space. Simulation results show that the polarizationdiversity based target recognition scheme can accurately identify the type of a target with less efforts of measurement. Simulation results also show that the proposed recognition scheme has good ability to tolerate random effects.
This dissertation is composed of seven chapters. Chapter 1 presents an introduction of this study. Chapter 2 gives basic theories utilized in this study. Chapter 3 shows the delayandsum DOA estimation of incoming signals is improved by our antenna mode switch technique and shows how the concept is implemented by a practical antenna design. In Chapter 4, we derive a novel formula for interpreting and predicting the improvement of DOA by using our antenna mode switch technique on delayandsum algorithms. In Chapter 5, the DOA estimation of incoming signals by minimum variance distortionless response method and antenna mode switch technique is presented. Chapter 6 shows the radar target recognition by polarizationdiversity RCS on PCA space. Finally, conclusions of this research are given in Chapter 7.

論文目次 
1 1
Introduction 1
11 Research Background 1
12 Motivation 3
13 Contribution 4
14 Dissertation overview 5
2 11
Basic Theory 11
21 Conventional Direction of Arrival Estimation 11
211 Delay and sum method 11
212 Capon method 12
22 Radar cross section 14
23 Polarization diversity technique 14
24 Principal component analysis 15
3 21
Reducing The Array Size for DOA Estimation by An Antenna Mode Switch Technique 21
31 Array analysis 22
32 Implementation of array element 26
33 Summary 28
4 46
A Novel Formula and Practical Antenna Design for 2Element Antenna Mode Switch Techniques of DOA Estimation 46
41 Formulations and analysis 47
42 Practical Implementation for DOA Estimation 51
43 Summary 53
5 64
Improvement of Capon DOA Estimation by Antenna Mode Switch Techniques 64
51 Formulations and analysis 64
52 Simulation result 66
53 Summary 69
6 75
Radar Target Recognition by Polarization Diversity RCS Including Random Effects 75
61 Formulations 76
62 Numerical simulation and analysis 79
63 Summary 82
7 95
Conclusion 95
71 Summary 95
72 Future work 97
References 100

參考文獻 
[1] J. C. Liberti and T. S. Rappaport, Smart Antenna for Wireless Communications IS95 and Third Generation CDMA Applications, NJ: Prentice Hall, 1999.
[2] K. C. Lee, “A genetic algorithm based direction finding technique with compensation of mutual coupling effects,” Journal of Electromagnetic Waves and Applications, Vol 17, pp. 16131624, 2003.
[3] S. C. Cheng and K. C. Lee., “Reducing the array size for DOA estimation by an antenna mode switch technique,” Progress In Electromagnetics Research, Vol. 131, pp.131117, 2012.
[4] S. C. Cheng and K. C. Lee, “A novel formula and practical antenna design for 2element antenna mode switch techniques of DOA estimation,” Electromagnetics , Vol 35, pp.124–137, 2015.
[5] G. T. Ruck, D. E. Barrick, W. D. Stuart and C.K. Krichbaum, Radar Cross Section Handbook, Vol. 1, Plenum, New York, 1970.
[6] J. Y. Jhang and K. C. Lee, “Array pattern optimization using electromagnetismlike algorithm,” AEUInt J Electron Commun, Vol. 63, pp. 491–496, 2009.
[7] R. Goossens and H. Rogier, “Directionofarrival and polarization estimation with uniform circular arrays in the presence of mutual coupling,” AEUInt. J. Electron Commun, Vol. 62, pp. 199–206, 2008.
[8] A. ElKeyia and T. Kirubarajan, “Adaptive beamspace focusing for direction of arrival estimation of wideband signals,” Signal Processing, Vol. 88, pp. 2063–2077, 2008.
[9] H. Zha, “Fast algorithms for directionofarrival finding using large ESPRIT arrays,” Signal Processing, Vol. 48, pp.111–121, 1996.
[10] N. Yilmazer and T. K. Sarkar, “2D unitary matrix pencil method for efficient direction of arrival estimation,” Digital Signal Processing, Vol. 16, pp. 767781, 2006.
[11] K. V. S. Babu, Y. Yoganandam and V. U. Reddy, “Adaptive estimation of eigensubspace and tracking the directions of arrival,” Signal Processing, Vol. 68, pp. 317–339, 1998.
[12] Y. Wua, G. Liaoa and H. C. So, “A fast algorithm for 2D directionofarrival estimation,” Signal Processing, Vol. 83, pp. 1827–1831, 2003.
[13] M. A. Beygi and A. Olfat, “Subspace based direction of arrival estimation of DSCDMA signals using orthogonal projection,” Signal Processing, Vol. 90, pp. 926–932, 2010.
[14] J. Verhaevert and E. V. Lil, “Capelin AV. Direction of arrival (DOA) parameter estimation with the SAGE algorithm,” Signal Processing, Vol. 84, pp. 619–629, 2004.
[15] M. Agrawala and S. Prasad, “Estimation of directions of arrival of wideband and wideband spread sources,” Signal Processing, Vol. 87, pp. 614–622, 2007.
[16] K. A. Gotsis, K. Siakavara and J. N. Sahalos, “On the direction of arrival (DOA) estimation for a switchbeam antenna system using neural networks,” IEEE Transaction on Antennas and Propagation, Vol. 57, pp. 13991411, 2009.
[17] M. R. Kamarudin, Y. I. Nechayev and P. S. Hall, “Onbody diversity and angle of arrival measurement using a pattern switching antenna,” IEEE Transaction on Antennas and Propagation, Vol. 57, pp. 964971, 2009.
[18] F. Gross, Smart Antennas for Wireless Communications with MATLAB, McGrawHill, 2005.
[19] T. K. Sarkar, M. SalazarPalma, M. C. Wicks and R. J. Bonneau, Smart Antennas, John Wiley & Sons, 2003.
[20] G. D. Durgin, SpaceTime Wireless Channels, Prentice Hall, 2003.
[21] R. C. Hansen, Phased Array Antenna, Wiley, New York, 1998
[22] J. Yeo and J. Lee, “Miniaturized LPDA antenna for portable direction finding applications,” ETRI Journal, Vol.34, pp. 118121, 2012.
[23] Y. Li, Y. J. Gu, Z. G. Shi and K. S. Chen, “Robust adaptive beamforming based on particle filter with noise unknown,” Progress In Electromagnetics Research, Vol. 90, pp. 151169, 2009.
[24] M. Mouhamadou, P. Vaudon and M. Rammal, “Smart antenna array patterns synthesis: null steering and multiuser beamforming by phase control,” Progress In Electromagnetics Research, Vol. 60, pp. 95106, 2006.
[25] Z. D. Zaharis, C. Skeberis and T. D. Xenos, “Improved antenna array adaptive beamforming with low side lobe level using a novel adaptive invasive weed optimization method,” Progress In Electromagnetics Research, Vol. 124, pp. 137150, 2012.
[26] T. Hong, M. Z. Song and Y. Liu, “RF directional modulation technique using a switched antenna array for communication and directionfinding applications,” Progress In Electromagnetics Research, Vol. 120, pp. 195213, 2011.
[27] P. Wounchoum, D. Worasawate, C. Phongcharoenpanich and M. Krairiksh, “A switchedbeam antenna using circumferentialslots on a concentric sectoral cylindrical cavity excited by coupling slots,” Progress In Electromagnetics Research, Vol. 120, pp. 127141, 2011.
[28] X. Wang, J. F. Chen, Z. G. Shi and K. S. Chen, “Fuzzycontrolbased particle filter for maneuvering target tracking,” Progress In Electromagnetics Research, Vol. 118, pp. 115, 2011.
[29] J. H. Lee, Y. S. Jeong, S. W. Cho, W. Y. Yeo and K. S. J. Pister, “Application of the Newton method to improve the accuracy of toa estimation with the beamforming algorithm and the music algorithm,” Progress In Electromagnetics Research, Vol. 116, pp. 475515, 2011.
[30] B. Tian, D. Y. Zhu and Z. D. Zhu, “A novel moving target detection approach for dualchannel SAR system,” Progress In Electromagnetics Research, Vol. 115, pp. 191206, 2011.
[31] P. Yang, F. Yang and Z. P. Nie, “DOA estimation with subarray divided technique and interporlated esprit algorithm on a cylindrical conformal array antenna,” Progress In Electromagnetics Research, Vol. 103, pp. 201216, 2010.
[32] A. N. Jabbar, “A novel ultrafast ultrasimple adaptive blind beamforming algorithm for smart antenna arrays,” Progress In Electromagnetics Research B, Vol. 35, pp. 329348, 2011.
[33] R. Mallipeddi, J. P. Lie, P. N. Suganthan, S. G. Razul and C. M. S. See, “Near optimal robust adaptive beamforming approach based on evolutionary algorithm,” Progress In Electromagnetics Research B, Vol. 29, pp. 157174, 2011.
[34] H. Peng, Z. Yang and T. Yang, “Calibration of a sixport receiver for direction finding using the artificial neural network technique,” Progress In Electromagnetics Research Letters, Vol. 27, pp. 1724, 2011.
[35] Y. Liu, Q. Wan and X. Chu, “A robust beamformer based on weighted sparse constraint,” Progress In Electromagnetics Research Letters, Vol. 16, pp. 5360, 2010.
[36] G. E. Atteia, A. A. Shaalan and K. F. A. Hussein. “ Wideband partiallycovered bowtie antenna for groundpenetratingradars,” Progress In Electromagnetics Research, Vol 71, pp. 211226, 2007
[37] S. W. Cho and J. H. Lee, “Efficient implementation of the capon beamforming using the levenbergmarquardt scheme for two dimensional AOA estimation,” Progress In Electromagnetics Research, Vol 137, pp 1934, 2013.
[38] J. Li and P. Stoica, Robust Adaptive Beamforming, WileyInterscience, New Jersey, 2006.
[39] S.D. Somasundaram, “Reduced dimension robust Capon beamforming for large aperture passive sonar arrays,” IET Radar Sonar Navig, Vol.5, pp. 707715, 2011.
[40] G. Hajduch, J.M.Le Caillec and R. Garello, “Airborne highresolution ISAR imaging of ship targets at sea,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 40, pp.378384, 2004.
[41] M. Tello, C. LopezMartinez and J.J. Mallorqui, “A novel algorithm for ship detection in SAR imagery based on the wavelet transform,” IEEE Geoscience and Remote Sensing Letters, Vol.2, pp.201205, 2005.
[42] H. Osman, S. Blostein and L. Gagnon, “Classification of ships in airborne SAR imagery using backpropagation neural networks,” SPIE Proc. Radar Processing, Technology, and Applications II, Vol.3161, pp126136, 1997.
[43] M. Menon, “Automatic ship classification system for ISAR imagery,” SPIE In Proceedings on Applications and Science of Artificial Neural Networks Orlando, Vol. 2492, pp, 373388, 1995.
[44] S. Musman, D. Kerr and C. Bachmann, “Automatic recognition of ISAR ship image,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 32, pp.13921402, 1996.
[45] S. C. Chan and K. C. Lee, “Angulardiversity target recognition by kernel scatterdifference based discriminate analysis on RCS,” International Journal of Applied Electromagnetics and Mechanics, Vol.42, pp. 409420, 2013.
[46] K. C. Lee, J. S. Ou and C. W. Huang, “Angulardiversity radar recognition of ships by transformation based approaches  including noise effects,” Progress In Electromagnetics ResearchPIER, Vol. 72, pp.141159, 2007.
[47] K. C. Lee and J. S. Ou, “Radar target recognition by using linear discriminant algorithm on angulardiversity RCS,” Journal of Electromagnetic Waves and Applications, Vol. 21, pp. 20332048, 2007.
[48] K. C. Lee, C. W. Huang and M. C. Fang, “Radar target recognition by projected features of frequencydiversity RCS,” Progress In Electromagnetic Research PIER, Vol. 81, pp.121133, 2008.
[49] C. W. Huang and K. C. Lee, “Application of ICA technique to PCA based radar target recognition,” Progress In Electromagnetic ResearchPIER, Vol.105, pp157170, 2010.
[50] C. W. Huang and K. C. Lee, “Frequencydiversity RCS based target recognition with ICA projection,” Journal of Electromagnetic Waves and Applications, Vol.24, pp.25472559, 2010.
[51] X. Li, G. Adamiuk, M. Janson, and T. Zwick, “Polarization system in ultrawideband image Systems,” Proceedings of 2010 IEEE International Conference on UltraWideband, 2010.
[52] K. C. Lee, “Polarization Effects on Bistatic Microwave Imaging of Perfectly Conducting Cylinders,” Master Thesis, National Taiwan University, Taipei, Taiwan, 1991.
[53] T. K. Moon and W. C. Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice Hall, 2000.
[54] R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd edition, John Wiley, New York, 2001.
[55] S. Theodoridis and K. Koutroumbas, Pattern Recognition, 2nd edition, Academic Press, Boston, 2003.
[56] G. A. Ioannidis and D. E. Hammers, “Optimum antenna polarizations for target discrimination in clutter,” IEEE Transactions on Antennas and Propagation, Vol. 27, pp. 357363, 1979.
[57] R. L. Haupt, Antenna Arrays, John Wiley & Sons, 2010.
[58] J. Wright, “A new model for sea clutter,” IEEE Transactions on Antennas and Propagation, Vol.16, pp. 217223, 1968.
[59] G. V. Trunk and S. F. George, “Detection of targets in nonGaussian sea clutter,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 6, pp. 620628, 1970.
[60] H. W. Melief, H. Greidanus, P. Genderen and P. Hoogeboom, “Analysis of sea spikes in radar sea clutter data,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, pp. 985993, 2006.
[61] N. W. Guinard and J. C. Daley, “An experimental study of a sea clutter model,” Proceedings of the IEEE, Vol. 58, pp. 543550, 1970.
[62] R. A. Paulus, “Evaporation duct effects on sea clutter,” IEEE Transactions on Antennas and Propagation, Vol.38, pp. 17651771, 1990.

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