傳統”時間式”濾波器方法不能被用來從與信號佔據同樣時間之頻帶上的干擾信號中過濾出所需之信號。藉著空間域上的濾波接收器，才能錯開來自不同空間上方位之同一時間頻帶上的干擾信號與所需信號。適應性天線陣列系統即靠著能自動地調整其天線接收的角度方位，來達到干擾消除的目的，增強所需信號的接收。在抑制一些非預期性的干擾，或是應用在多用戶無線通訊系統檢測上面，現在已經有很多建立在線性限制性上面的適應性陣列天線波束形成演算法被提出來討論。反向QR分解之遞迴式最小平方和(IQRD-RLS)演算法有不需要每次往後回代求出最小平方和(LS)權重向量、數值上穩定、優於最小均方(LMS)系列演算法的收斂速度、穩態均方誤差和參數追蹤能力等等優點。在本論文中，提出一個新的應用結合廣義旁波帶消除器(GSC)架構與IQRD-RLS演算法為GSC-IQRD-RLS演算法。此演算法可以保留有GSC架構簡單，計算量較少，可將線性限制性最佳化問題轉變成標準的最佳化濾波問題等等種種優點，但在性能表現上仍等同於傳統的線性限制性(LC)IQRD-RLS演算法。

The conventional temporal filtering approach cannot be used to separate signal from interference which occupies the same temporal frequency band as signal. Using a spatial filtering at the receiver can separate signals from interference that originates from different spatial location. Many adaptive array beamforming algorithms, based on linear constraints, have been proposed for suppressing undesired interference and being applied to wireless communication systems for multiuser detection. The adaptive array system can be employed to automatically adjust its directional to achieve the purpose that nulls the interferences or jammers and thus, enhances the reception of the desired signal. Inverse QR Decomposition Recursive Least-square (IQRD-RLS) algorithm has many advantages such as where the LS weight vector be computed without back substitution, a well known numerical stable algorithm and offering better convergence rate, steady-state means-square error, and parameter tracking capability over the adaptive least mean square (LMS) based algorithms. In this thesis, a new application, GSC-IQRD-RLS combining Generalized Sidelobe Canceller (GSC) and IQRD-RLS algorithm, is developed. It preserves the advantages of GSC such as simple structure, less computations, and converts a linearly constrained optimization problem into a standard optimum filtering problem. But the performance is equivalent between GSC-IQRD-RLS and LC-IQRD-RLS algorithms.

目錄
誌謝 i
中文摘要 ii
英文摘要 iii
目錄 iv
圖表目錄 vi
第一章 簡介 1
第二章 傳統適應性之線性限制性反向QR分解之遞迴式最小平方和波束形成演算法
2.1 簡介 4
2.2 標準適應性陣列天線波束形成系統的模型描述 4
2.3 適應性之線性限制性反向QR分解之遞迴式最小平方和演算法 5
2.3.1 線性限制性反向QR分解之遞迴式最小平方和波束形成器之最佳解 5
2.3.2 反向QR分解之遞迴式最小平方和演算法的遞迴式實現 8
第三章 利用反向QR分解之遞回式最小平方和演算法於修正式廣義旁波帶消除器
3.1 簡介 13
3.2 GSC架構在線性限制性最小變異數窄頻波束形成器之介紹 14
3.3 利用反向QR分解之遞迴式最小平方和演算法於修正式廣義旁波帶 消除器 21
3.4 電腦模擬結果 26
第四章 結論 38
參考文獻 41

[1] O. L. Frost III, ”An algorithm for linearly constraint adaptive array processing,” Proc. IEEE, vol. 60, pp. 926-935, Aug. 1972.
[2] L. J. Griffths and J. W. Jim, “An alternative approach to linearly constraint adaptive beamforming,” IEEE Trans. Antennas Propagat., vol.AP-30,pp. 27-34, Jan. 1982.
[3] S. J. Chern and C. Y. Sung, “The performance of hybrid adaptive beamforming algorithm for jammers suppression,” IEEE Trans. Antennas Propagat., vol. 42,pp. 1223-1232, Sept. 1994.
[4] L. S. Resende, J. M. T. Romano, and M. G. Bellanger, ”A robust FLS algorithm for LCMV adaptive broad band beamformer,” in Proc. IEEE Int. Conf. Acoustics, Speed, and Signal Processing, vol. 3, pp.1826-1829, 1996.
[5] S. Haykin, Adaptive Filter Theory, 3rd ed. Englewood Cliffs, NJ:Prentice-Hall, 1996.
[6] S. J. Chern and C. Y. Chang, “Adaptive Linearly Constrained Inverse QRD-RLS Beamforming Algorithm for Moving Jammers Suppression,” IEEE Trans. on Antennas Propagation, vol. 50,, no. 8, pp.1138-1150, August 2002.
[7] J. G. McWhirter and T. J. Shepherd, ”Systolic array processor for MVDR beamforming,” Proc. Inst. Elect. Eng. , pt. F, vol. 136, pp. 75-80, Apr. 1989.
[8] J. M. Cioffi and T. Kailath, “Fast RLS transversal filters for adaptive filtering,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-32, pp. 304-337, June 1984.
[9] J. M. Cioffi, “Limited precision effects for adaptive filtering,” IEEE Trans. Circuits Syst., vol. CAS-34, pp. 821-833, July 1987.
[10] P. Fabre and C. Gueguen, “Improvement of fast recursive least squares algorithms via normalization: A comparative study,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-34, pp. 296-308, Apr. 1986.
[11] H. Leung and S. Haykin, “Stability of recursive QRD-RLS algorithm using finite precision systolic array implementation,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, pp. 760-763, May 1989.
[12] P. A. Regalia and M. G. Bellanger, “On the duality between fast QR methods and lattice methods in least squares adaptive filtering,” IEEE Trans. Signal Processing, vol. 39, pp. 879-891, Apr. 1991.
[13] S. T. Alexander and A. L. Ghirnikar, “A method for recursive least squares filtering based upon an inverse QR decomposition,” IEEE Trans. Signal Processing, vol. 41, pp. 20-30, Jan. 1993.
[14] C. T. Pan and R. J. Plemmons, “Least squares modifications with factorization: Parallel implications,” J. Comput. Applied Math., vol. 27, no. 1-2, pp. 109-127, Arp. 1989.
[15] L. S. Resende, J. T. Romano, and M. G. Bellanger, ”A fast least-squares algorithm for linearly constrained adaptive filtering,” IEEE Trans. Signal Processing, vol. 44, pp. 1168-1174, May 1996.
[16] J. B. Schodorf and D. W. Williams, “Array processing techniques for multiuser detection,” IEEE Trans. Commun., vol. 45, pp. 1375-1378, Nov. 1997.