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研究生:陳慶永
研究生(外文):Ching-Yung Chen
論文名稱:以基於高階統計量之反濾波器準則於盲蔽多輸入多輸出系統鑑別,信號源分離與時間延遲估計
論文名稱(外文):BLIND MIMO SYSTEM IDENTIFICATION, SOURCE SEPARATION AND TIME DELAY ESTIMATION USING HIGHER-ORDER STATISTICS BASED INVERSE FILTER CRITERIA
指導教授:祁忠勇
指導教授(外文):Chong-Yung Chi
學位類別:博士
校院名稱:國立清華大學
系所名稱:電機工程學系
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:2003
畢業學年度:91
語文別:英文
論文頁數:151
中文關鍵詞:盲蔽系統鑑別高階統計量盲蔽等化盲蔽信號源分離多輸入多輸出系統多用戶檢測無線通訊時間延遲估計
外文關鍵詞:blind system identificationhigher-order statisticsblind equalizationblind source separationmultiple-input multiple-output systemmultiuser detectionwireless communicationstime delay estimation
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多輸入多輸出(MIMO)線性非時變系統之盲蔽鑑別(BID)的目的在於僅使用一未知系統的非高斯輸出量測向量資料來估計此未知系統。大部份現存的盲蔽鑑別演算法則都是基於無雜訊的假設下被發展出來,而且這些演算法則對於該未知多輸入多輸出系統,以及對其驅動輸入之頻譜需要嚴格的條件限制。
本論文研究使用受雜訊干擾之量測資料並將雜訊效應列入考量在單一輸入多輸出(SIMO)系統與多輸入多輸出系統之盲蔽鑑別。基於根據 Tugnait 與 Chi 等人提出的使用高階統計量之反濾波器準則(HOS-IFC)所設計的最佳等化器與未知系統之間的關係,本論文證明了使用量測資料之交互頻譜矩陣與基於使用高階統計量之反濾波器準則所設計的最佳等化器對於一個單一輸入多輸出系統之可鑑別性,同時提出了一種盲蔽鑑別演算法則,適用於鑑別由時間上獨立(temporally independent)之信號所驅動的單一輸入多輸出系統。接著,本論文進一步使用基於高階統計量之反濾波器準則與 Qiu 等人提出的最大公因式演算法則提出了一種兩部驟的盲蔽鑑別演算法則,適用於鑑別由空間上獨立(spatially independent)而時間上具有色彩或獨立(temporally colored or independent)之多個信號所驅動的多輸入多輸出系統,並證明其系統之可鑑性。所提出的兩部驟的盲蔽鑑別演算法則只要求該未知系統之轉移函數的每一行向量元素間為互質,因此比大部份現存的盲蔽鑑別演算法則更具有彈性。
在另一方面,本論文也提出了使用基於高階統計量之反濾波器準則之多輸入多輸出系統盲蔽鑑別演算法則的一些應用,包括:同時估計多個時間延遲(time delays),盲蔽波束形成,在多重路徑環境下之盲蔽信號源分離,以及使用多重天線於非同步直接序列分碼多工系統(DS/CDMA)之多用戶檢測。
Blind identification (BID) of a multiple-input multiple-output (MIMO) linear time-invariant system is a problem of estimating the unknown system with only a set of non-Gaussian vector output measurements of the system. Most of the existing BID
algorithms are developed under the noise-free assumption and
require stringent conditions on the unknown MIMO system as well as on the spectra of the driving inputs of the system.
With noise effects taken into account, this thesis studies blind
single-input multiple-output (SIMO) and MIMO system identification with noisy measurements. Based on the relationship between the unknown system and the optimum equalizer associated with the higher-order statistics based inverse filter criteria (HOS-IFC) proposed by Tugnait and Chi et al., a BID algorithm for an SIMO system driven by a temporally independent input signal is proposed together with the proof of the identifiability for an SIMO system using the cross-spectral matrix of the measurements and the optimum equalizer associated with the HOS-IFC. Then, a two-step BID algorithm for an MIMO FIR system driven by spatially independent and temporally colored or independent inputs is
further proposed using the HOS-IFC and Qiu et al.''s greatest
common divisor computation algorithm together with the proof of
the system identifiability. The proposed two-step BID algorithm
only requires coprimeness of the components in each column of the system transfer function and is thus more flexible than most of the existing BID algorithms.
On the other hand, some applications of the BID algorithms for
MIMO systems using the HOS-IFC are also presented in this thesis
including simultaneous estimation of multiple time delays, blind
beamforming, blind source separation in multipath and multiuser
detection for asynchronous direct sequence/code division multiple access (DS/CDMA) systems using multiple antennas.
\contentsline{chapter}%
{\uppercase{Chinese Abstract}}{ ageref{cabs}}%
\contentsline{chapter}%
{\uppercase{Abstract}}{ ageref{abs}}%
\contentsline{chapter}%
{\uppercase{Acknowledgments}}{ ageref{ack}}%
\contentsline{chapter}%
{\uppercase{Contents}}{ ageref{con}}%
%%%%%%%%%%%%%%%%%%
\baselineskip 24pt \vspace*{0.75\baselineskip}
\contentsline{chapter}%
{\numberline{1}\uppercase{Introduction}}{ ageref{ch1}}%
\contentsline{chapter}%
{\numberline{2}\uppercase{Review of the HOS-IFC for Blind Equalization}}{ ageref{ch2}}%
\contentsline{section}%
{\numberline{2.1}Model Assumptions and Problem Statement}{ ageref{sec2.1}}%
\contentsline{section}%
{\numberline{2.2}HOS-IFC for Blind Equalization}{ ageref{sec2.2}}%
\contentsline{subsection}%
{\numberline{2.2.1}Estimation of One Source Signal}{ ageref{sec2.2.1}}%
\contentsline{subsection}%
{\numberline{2.2.2}Estimation of All Source Signals}{ ageref{sec2.2.2}}%
\contentsline{section}%
{\numberline{2.3}Properties about the HOS-IFC Based Equalizers}{ ageref{sec2.3}}%
\contentsline{section}%
{\numberline{2.4}Summary}{ ageref{sec2.4}}%
\contentsline{section}%
{\numberline{2A}Chi and Chen''s Fast Iterative
MIMO Blind Deconvolution Algorithm}{ ageref{sec2A}}%
\contentsline{chapter}%
{\numberline{3}\uppercase{Blind Identification of SIMO Systems}}{ ageref{ch3}}%
\contentsline{section}%
{\numberline{3.1}Model Assumptions and Problem Statement}{ ageref{sec3.1}}%
\contentsline{section}%
{\numberline{3.2}Relationship between the Unknown System and the Optimum Equalizer
Designed by the HOS-IFC}{ ageref{sec3.2}}%
\contentsline{section}%
{\numberline{3.3}BID of SIMO Systems}{ ageref{sec3.3}}%
\contentsline{subsection}%
{\numberline{3.3.1}Analysis about the
Systems Solved from (\ref{eq3:3Bhhh})}{ ageref{sec3.3.1}}%
\contentsline{subsection}%
{\numberline{3.3.2}Algorithm for Obtaining
the System Estimate from (\ref{eq3:3Bhhh})}{ ageref{sec3.3.2}}%
\contentsline{section}%
{\numberline{3.4}Simulation Results}{ ageref{sec3.4}}%
\contentsline{section}%
{\numberline{3.5}Summary}{ ageref{sec3.5}}%
\contentsline{section}%
{\numberline{3A}Proof of Property 3.1}{ ageref{sec3A}}%
\contentsline{section}%
{\numberline{3B}Proof of Lemma 3.1}{ ageref{sec3B}}%
\contentsline{section}%
{\numberline{3C}Proof of Property 3.2}{ ageref{sec3C}}%
\contentsline{section}%
{\numberline{3D}Proof of Property 3.3}{ ageref{sec3D}}%
\contentsline{chapter}%
{\numberline{4}\uppercase{Blind Identification of MIMO FIR Systems}}{ ageref{ch4}}%
\contentsline{section}%
{\numberline{4.1}Model Assumptions and Problem Statement}{ ageref{sec4.1}}%
\contentsline{section}%
{\numberline{4.2}BID of the Combined MIMO System}{ ageref{sec4.2}}%
\contentsline{subsection}%
{\numberline{4.2.1}Relationship between the Unknown System and
the Optimum Equalizers Designed by the HOS-IFC}{ ageref{sec4.2.1}}%
\contentsline{subsection}%
{\numberline{4.2.2}Analysis about the
Systems Solved from (\ref{eq4:Vandmmse2})}{ ageref{sec4.2.2}}%
\contentsline{subsection}%
{\numberline{4.2.3}Algorithm for Obtaining
the System Estimate from (\ref{eq4:Vandmmse2})}{ ageref{sec4.2.3}}%
\contentsline{section}%
{\numberline{4.3}BID of the MIMO system ${\mathbf F}[n]$}{ ageref{sec4.3}}%
\contentsline{section}%
{\numberline{4.4}Simulation Results}{ ageref{sec4.4}}%
\contentsline{section}%
{\numberline{4.5}Summary}{ ageref{sec4.5}}%
\contentsline{section}%
{\numberline{4A}Proof of Property 4.1}{ ageref{sec4A}}%
\contentsline{section}%
{\numberline{4B}Proof of Property 4.2}{ ageref{sec4B}}%
\contentsline{section}%
{\numberline{4C}Proof of Lemma 4.1}{ ageref{sec4C}}%
\contentsline{section}%
{\numberline{4D}Proof of Property 4.3}{ ageref{sec4D}}%
\contentsline{section}%
{\numberline{4E}Initial Conditions for ${\mathbf v}_k[n]$''s Associated with $J_{p,p}$}{ ageref{sec4E}}%
\contentsline{section}%
{\numberline{4F}Qiu et al.''s GCD Computation Algorithm}{ ageref{sec4F}}%
\contentsline{chapter}%
{\numberline{5}\uppercase{Simultaneous Estimation of Multiple Time Delays}}{ ageref{ch5}}%
\contentsline{section}%
{\numberline{5.1}Model Assumptions and Problem Statement}{ ageref{sec5.1}}%
\contentsline{section}%
{\numberline{5.2}MTDE Approach with Spatial Diversity Gain}{ ageref{sec5.2}}%
\contentsline{section}%
{\numberline{5.3}Simulation Results}{ ageref{sec5.3}}%
\contentsline{section}%
{\numberline{5.4}Summary}{ ageref{sec5.4}}%
\contentsline{chapter}%
{\numberline{6}\uppercase{Blind Beamforming and Maximum Ratio Combining for
Source Separation in Multipath}}{ ageref{ch6}}%
\contentsline{section}%
{\numberline{6.1}Model Assumptions and Problem Statement}{ ageref{sec6.1}}%
\contentsline{section}%
{\numberline{6.2}Blind Source Separation Algorithm}{ ageref{sec6.2}}%
\contentsline{subsection}%
{\numberline{6.2.1}FKMA}{ ageref{sec6.2.1}}%
\contentsline{subsection}%
{\numberline{6.2.2}BMRC Method}{ ageref{sec6.2.2}}%
\contentsline{subsection}%
{\numberline{6.2.3}MSS Algorithm}{ ageref{sec6.2.3}}%
\contentsline{section}%
{\numberline{6.3}Blind Multiuser Detection for Asynchronous DS/CDMA
Systems Using Multiple Antennas}{ ageref{sec6.3}}%
\contentsline{subsection}%
{\numberline{6.3.1}Signature Waveform Matched
Filtering Based MIMO Models}{ ageref{sec6.3.1}}%
\contentsline{subsection}%
{\numberline{6.3.2}Chip Waveform Matched
Filtering Based MIMO Model}{ ageref{sec6.3.2}}%
\contentsline{section}%
{\numberline{6.4}Simulation Results}{ ageref{sec6.4}}%
\contentsline{section}%
{\numberline{6.5}Summary}{ ageref{sec6.5}}%
\contentsline{section}%
{\numberline{6A}Proof of Theorem 6.1}{ ageref{sec6A}}%
\contentsline{section}%
{\numberline{6B}Proof of (F6.4)}{ ageref{sec6B}}%
\contentsline{chapter}%
{\numberline{7}\uppercase{Conclusions and Future Research}}{ ageref{ch7}}%
\contentsline{chapter}%
{\uppercase{Bibliography}}{ ageref{bib}}%
\contentsline{chapter}%
{\uppercase{Publication List of the Author}}{ ageref{publist}}%
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