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研究生:黃俊諺
研究生(外文):Jun-Yen Huang
論文名稱:適應性特徵分解演算法於訊號源方位
論文名稱(外文):Implementation of Direction of Arrival Estimation of Signals by Adaptive Eigendecomposition Algorithms
指導教授:張麗娜張麗娜引用關係
指導教授(外文):Lena Chang
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:導航與通訊系
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:2005
畢業學年度:93
語文別:中文
論文頁數:118
中文關鍵詞:方位估測近場多重路徑適應性特徵分解演算法數位訊號處理器水槽
外文關鍵詞:Direction of Arrival (DOA)near fieldmultipathDigital Signal ProcessorAdaptive Eigendecomposition algorithmwatertank
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在陣列訊號處理的應用上,訊號源方位估測的研究一直是雷達、天文探測、聲納、地震學與振動噪音等主要探討的方向。近幾年來,使用高解析度的陣列訊號處理受到了許多研究學者的注意,且應用在方位估測上的許多演算法被廣泛的推導與驗証,其中以特徵空間的MUSIC演算法最常被引用,但是大多數文獻中所提出的演算法,都只是經由電腦模擬驗證其推論的正確性,往往忽略實際環境情況下所存在的問題。本論文乃針對水下的環境,提出一適應性方位估測法,並以德州儀器TMS320C6701之數位信號處理器以及軟體發展環境CCS來實現所提之方位估測法,驗證此法於實際水下環境中之可行性及效能。
為克服水中近場及多重路徑干擾的問題,我們首先提出以遠場校正法及空間平滑法結合適應性特徵分解演算法。遠場校正法可修正適應性特徵分解演算法所調整的特徵向量,使其能估測出近場訊號源方位;而空間平滑法可解決因多重路徑干擾所造成訊號子空間維度退化的問題,故可達正確估測同調訊號源方位的目的。此外,論文中推導出方位搜尋頻譜多項式根的公式解,並利用此解作為解根法,如牛頓法之初始值,以達快速收斂之目的。經過電腦模擬結果顯示論文所提之適應性方位估測法可有效地解決近場及多重路徑的影響,除了能正確估測出固定訊號源方位,並可達訊號源追蹤的目的。
接著,論文藉由硬體實現的方式來驗證此適應性方位估測法的效能。我們使用德州儀器的TMS320C6701數位訊號處理器為核心,並以軟體發展環境CCS來實現適應性方位估測法。因此適應性特徵分解演算法是以實數運算方式處理,能減少計算上的複雜度,在程式撰寫上比較容易實現;且此適應性方位估測法具有快速收斂的特性,故可達適應性方位估測和追蹤之目的。經由水槽實驗結果,證實本論文所提之適應性方位估測法的確可適用於水下環境。
The estimation of direction-of-arrival (DOA) has been widely applied in the field of radar, sonar, communications and seismology. Recently, high-resolution eigenspace-based techniques have been developed for DOA estimation. These eigenspace-based methods, such as MUSIC, have been verified to be efficient in estimating the bearings of signals by some computation simulations. However, these methods are suffered from some realistic problems, such as near-field sources, multipath interference and computation complexity when they are applied in real environments. In the thesis, we propose a fast adaptive eigenspace-based algorithm for DOA estimation and tracking. To verify the feasibility and efficiency of the proposed method in underwater environments, we implement the adaptive DOA estimation method by utilizing the TI’s TMS320C6701 Digital Signal Processor.
The proposed DOA estimation method is based on a real-valued forward-backward adaptive eigendecomposition(RFBAE) algorithm, which requires only real operations in calculating the eigencomponents of signal subspace. To alleviate the performance degradation caused by near-field sources and multipath interference, we develop a far-field revision procedure and a smoothed based RFBAE algorithm. The far-field revision procedure can adjust the near-field steering vector to be the far-field steering vector with same DOA allocation. The smoothed based RFBAE algorithm combines the spatial smoothing method and the RFBAE for coherent sources estimation. Moreover, to facilitate the DOA estimation in real-time implementation, we derive the closed form for the roots of null spectral polynomial in Root-MUSIC. Utilizing the derivational roots as the initial values of Newton method, we may accelerate the DOA estimation. Simulations validate the proposed adaptive DOA estimation method can resolve the coherent near-field sources efficiently.
Then, we implement the proposed method by TI’s TMS320C6701 Digital Signal Processor. The program realization of the proposed method requires only real operations and less computation complexity in each iteration. The rapid convergence rate of the proposed method provides the accuracy of DOA estimation and tracking. Watertank experiments confirm the feasibility and efficiency of the proposed adaptive DOA estimation method in underwater environments.
致謝
中文摘要
英文摘要
目錄
圖目錄

第一章 緒論
1.1 陣列訊號處理簡介
1.2 方位估測背景及文獻回顧2
1.3 研究動機4
1.4 各章節內容概述

第二章 以特徵結構為基礎之方位估測
2.1 均勻線性陣列模型
2.2 MUSIC演算法
2.3 Root-MUSIC演算法
2.4 一般膨脹演算法
2.5 適應性特徵分解演算法
2.6 電腦模擬
2.6.1 模擬2.1
2.6.2 模擬2.2
2.6.3 模擬2.3
2.6.4 模擬2.4
2.6.5 模擬2.5
2.6.6 模擬2.6
2.7 討論與分析

第三章 數位信號處理簡介與實現設計
3.1 數位信號處理的發展
3.2 DSP在各領域的應用
3.3 TMS320C6701簡介
3.4 發展環境
3.5 硬體實現設計
3.5.1 TMS320C6701的資料型態
3.5.2 定點數與浮點數
3.5.3 DSP設計工具與輸入方式
3.5.4程式碼最佳化

第四章 適應性方位估測之實現
4.1 近場訊號模型
4.1.1 遠場近似法
4.1.2 遠場校正法
4.2 多重路徑資料模型
4.2.1 空間平滑法
4.2.2 空間平滑適應性特徵分解演算法
4.3 軟體架構
4.3.1 Hilbert轉換
4.3.2 牛頓法求解多項式根及角度計算
4.3.3 正割法求解多項式根
4.3.4 公式求解多項式根
4.4 硬體架構
4.5 電腦模擬
4.5.1 模擬4.1
4.5.2 模擬4.2
4.5.3 模擬4.3
4.5.4 模擬4.4
4.5.5 模擬4.5
4.5.6 模擬4.6
4.5.7 模擬4.7
4.5.8 模擬4.8
4.5.9 模擬4.9
4.6 水槽實驗
4.6.1 實驗(一)
4.6.2 實驗(二)
4.6.3 實驗(三
4.7 討論與分析
4.7.1 近場訊號源模擬結論
4.7.2 同調訊號源模擬結論
4.7.3 近場且同調訊號源及近場且同調移動訊號源模擬結論
4.7.4 水槽實驗結論
4.7.5 綜合分析與討論


第五章 結論及未來研究方向
5.1 結論
5.2 未來研究與發展方向

參考文獻
[1] R. O. Schmidt, “Multiple Emitter Location and Signal Parameter Estimation,” in Proc. RADC Spectral Estimation Workshop, Rome, NY, pp243-258, 1979.
[2] A. J. Barabell, “Improving the Resolution Performance of Eigenstructure Based Direction-Finding Algorithm,” Proc. ICASSP, Boston, MA, pp. 336-339, April 1983.
[3] J.F. Yang and M. Kaveh, “An adaptive eigensubspace algorithms for direction or frequency estimation and tracking,” IEEE Transactions on Acoustics Speech and Signal Processing, vol. 36, no. 6, pp. 241-251, June 1988.
[4] Lena Chang, “Adaptive eigendecomposition algorithms for eigenstructure-based array signal processing,” Journal of the Chinese Institute of Engineers, vol. 20, no. 4, pp. 365-375, March 1997.
[5] S. Haykin, Adaptive Filter Theory, 3rd Edition, Prentice-Hall, New Jersey, 1996.
[6] P. A. Thompson, “An adaptive spectral analysis technique for unbiased frequency estimation in the presence of white noise,” in Proc. 13th Asilomar Conf. Circuits, Syst. Comput., Pacific Grove, pp. 529-533, CA, 1980.
[7] Huarng, K.C. and C.C. Yeh, “Adaptive Beamforming with Conjugate Symmetric Weight,” IEEE Transactions on Antennas and Propagation, vol. 39, no. 7, pp. 926- 932, July 1991.
[8] R. Roy, and T. Kailath, “ESPRIT-Estimation of Signal Parameters Via Rotational Invariance Techniques,” IEEE Transactions Acoustics, Speech, Signal Processing, vol.37, no. 7, pp. 984-995, July 1989
[9] T. J. Shan, M. Wax and T. Kailath, “On spatial smoothing for direction of arrival estimator of coherence signals,” IEEE Transactions Acoustic, Speech, Signal Processing, vol. ASSP-33, pp. 806-811, August 1985
[10] Nitzberg, R., “Application of Maximum Likelihood Estimation of Persymmetric
Covariance Matrices to adaptive Processing,” IEEE Transactions Aerosp. Electron.
Syst., vol. AES-36, no. 1, pp. 124-127, January 1980.
[11] 謝澄漢、董勝源, “TI 6711 DSP入門與實作,” 宏友圖書開發股份有限公司, 民國九十二年四月.
[12] 吳賢財, “TI C6000 DSP入門實務,” 滄海書局,民國九十二年一月.
[13] TMS320C6000 CPU and Instruction Set Reference Guide. Texas instruments, Literature no. SPRU189F, October 2000.
[14] TMS320C6X Optimizing C Complier User’s Guide, Texas instruments, Literature no. SPRU187B, July 1997.
[15] M. Wax and I. Zisking, “Detection of number of coherent signals by the MDL principle,” IEEE Transactions Acoustics, Speech, Signal Processing, vol. ASSP-37, pp. 1190-1196, August 1989.
[16] M. Wax and T. Kailath, “Detection of signal by information theoretic criteria,” IEEE Transactions Acoustics, Speech, Signal Processing, vol. ASSP-33, pp. 387-392, April 1985.
[17] B. Nobel and J. W. Daniel, Applied Linear Algebra, Englewood Cliffs, Prentice Hall, 1988.
[18] M. P. Lotter and P. Van Rooyen, ”An overview of space division multiple access techniques in cellular systems”, Communications and Signal Processing, pp. 161-164, September 1998.
[19] TMS320C6000 Technical Brief. Texas instruments, Literature no. SPRU197D, February 1999.
[20] TMS320C6000 Peripherals Reference Guide. Texas instruments, Literature no. SPRU190C, April 1999.
[21] B.D. steinberg, Principles of Aperture and Array System Design, Wiley, New York, 1984.
[22] Code Composer Studio User’s Guide. Texas instruments, Literature no. SPRU328B, February 2000.
[23] Q. S. Ren and A.J. Willis, “Fast root MUSIC algorithm,” IEEE Electronics Letters, Vol. 33, no. 6, pp. 450-451, March 1997.
[24] J.Sanchez-Aranjo and S. Marcos, “Statistical analysis of propagator method for DOA estimator without eigendecomposition,” Statistical Signal and Array Processing, 1996. Proceedings., 8th IEEE Signal Processing Workshop on, pp. 570-573, June 1996.
[25] V. F. Pisarenko, “The retrieval of harmonics from a covariance function,” Geophys. J. Royal Astron. Soc., vol. 33, pp. 347-366, 1973.
[26] Y. D. Huang and M. Barkat,“Near-field Multiple Source Location by Passive Sensor Array,” IEEE Transactions on Antenna and Prop., vol. 37, pp. 968-974 July 1991.
[27] Marius Pesavento, Alex B. Gershman, and Martin Haardt, “Unitary Root-Music with a Real-Valued Eigendecomposition : A Theoretical and Experimental Performance Study,” IEEE Transactions on Signal Processing, Vol. 48, No. 5. pp. 1306-1314, May 2000.
[28] D. A. Linebarger, R. D. DeGroat, and E. m. Dowling, “Efficient Direction-Finding Methods Employing Forward-Backward Averaging,” IEEE Transactions on Signal Processing, Vol. 33. No. 6, pp. 450-451, March 1997.
[29] J. F. Yang and H. J. Lin, “Adaptive high-resolution algorithms for tracking nonstationary sources without the estimation of source number,” IEEE Trans. Signal Processing, vol. 42, pp. 563-571, March 1994.
[30] 簡聰海,“數值分析使用C語言,”全華科技圖書股份有限公司, 民國八十九年十月.
[31] J. H. Lee, Y. M. Chen and C. C. Yeh, “A covariance approximation method for near-field direction-finding using a uniform linear array,” IEEE Transactions Signal Processing, vol. 43, pp. 1293-1298, May 1995.
[32] R. T. Compton Jr., Adaptive Arrays-Concepts and Performance, Prentice-Hall, Englewood Cliffs, 1988.
[33] M. Rossi, Acoustic and Electroacoustics, Artech House, Norwood, MA, 1988.
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