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研究生:徐明志
論文名稱:應用Karhunen-Loeve展開於時間頻率分析訊號辨識技術之研究
論文名稱(外文):Karhunen-Loeve Expansion with Time-Frequency Analysis Technique for Signal Recognition
指導教授:張順雄張順雄引用關係
學位類別:碩士
校院名稱:國立海洋大學
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:2002
畢業學年度:90
語文別:中文
論文頁數:78
中文關鍵詞:時頻分佈函數非穩態訊號訊號辨識
相關次數:
  • 被引用被引用:2
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時頻分佈函數能精確地將訊號映射至二維的時間頻率域,故近年來在雷達、聲納、
地震震源探勘及水中聲學等非穩態訊號的範疇中應用極廣。目前的高解析時頻分佈皆以柯漢家族時頻分佈函數結構為基礎,
但時頻分佈函數由雙線性特性所構成,因而會產生類似雜訊的交關項因子,有效的消除交關項,
且讓主要訊號更加集中是現今努力的目標。本論文比較各類的核心函數對其訊號解析度的優劣點,
以提高時頻訊號解析能力。訊號經過時頻分析前置處理後,再利用KL展開特徵萃取,
由於隨機訊號主要能量若集中於主要的特徵向量,則依據此主要特徵向量作為訊號間辨識的特徵,
當訊雜比為10dB時, 此系統的辨識率可達到百分之80。

The time-frequency distribution(TFD) can accurately transform signals into two-dimensional
time-frequency domain. It has extensive application in the nonstationary fields of sonar,
seismology and underwater acoustic. Most of the current high resolution time-frequency
methods are based on the structure of the Cohen time-frequency distribution. However,
since the time-frequency distribution has the bilinear property which will produce the
cross terms factor treated it as the noise. It is important to sharp the signal and
reduce the cross term efficiently. In this thesis, we compare the signal resolution of different
kind kernel function, and try to find a way to promote the ability of time-frequency resolution.
The received signal will be analyzed with time-frequency analyzed first, and the eign vector of
analysis signal will be extracted by Karhunen-Loeve expansion. The eign vector will be compared
with database. The probability of recognition can reach 80 percent at SNR=10 dB.

第一章 序論
1.1 ~ 背景簡介
1.2 ~ 研究背景與動機
1.3 ~ 各章節內容概述
第二章 時頻分佈函數
2.1 ~ 傅立葉轉換
2.2 ~ 短時距傅立葉轉換
2.3 ~ 時頻分佈函數
2.4 ~ 時頻分佈函數的種類
2.5 ~ 時頻分佈函數之性質
2.6 ~ 離散時頻分佈函數
2.7 ~ 錐形時頻分佈函數
2.71 ~ 定義
2.72 ~ 特殊性質
2.8 ~ 可適性錐形時頻分佈函數
2.81 ~ 可適性的方法
2.82 ~ 可適錐形時頻分佈函數
2.9 ~ 各類型的核心函數分析比較
第三章 Karhunen-Loeve展開於時頻分析訊號辨識
3.1 ~ 時頻分佈函數於語音訊號分析
3.2 ~ Karhunen-Loeve展開(Expansion)
3.3 ~ 選取較精確特徵向量及臨限值的判定
3.31 ~ 選取較精確特徵向量
3.32 ~ 臨限值的判定
3.4 ~ 偵測率
第四章 使用者搜尋介面之設計
4.1 ~ 資料庫系統存取方法
4.2 ~ 系統作業流程
4.21 ~ 系統架構
4.22 ~ 介面操作介紹
第五章 結論及未來研究方向

[1]E. Wigner, \On the quantum correction for thermodynamic equilibrium,"
Phys. Rev., vol. 40, pp. 749-759, 1932.
[2] S. G. Mallat, \A theory of multiresolution signal decomposition: The wavelet
representation," IEEE Trans. on Pattern Analysis and Machine Intelligence,
vol. 11, no. 7, pp. 674-693, July 1989.
[3] L. Cohen, Time-Frequency Analysis, Prentice-Hall, Englewood Clis, New
Jersey, 1994.
[4] L. Cohen, \Time-frequency distribution-A review," Proc. IEEE, vol. 77, no.
7, pp. 941-981, July 1989.
[5] H. Choi and W. J. Williams, \Improved time-frequency representation of
multicomponent signals using exponential kernels," IEEE Trans. on Acoust.,
Speech, and Signal Processing, vol. 37, no. 6, pp. 862-871, June 1989.
[6] J. Jeong and W. J. Williams, \Kernel design for reduced interference distributions,"
IEEE Trans. on Signal Processing, vol. 40, no. 2, pp. 402-412, Feb.
1992.
[7] J. A. Draidi, L. M. Khadra and M. A. Khasawneh, \Generalized cone-shaped
kernels for time-frequency distributions," Proc. IEEE ICASSP-95, pp. 1880-
1883, April 1995.
[8] Y. Zhao, L. E. Atlas and R. J. Marks, \The use of cone-shaped kernels for
generalized time-frequency representations of nonstationary signals," IEEE
Trans. on Acoust., Speech, and Signal Processing, vol. 38, no. 7, pp. 1084-
1091, July 1990.
[9] Michel Loeve, Probability Theory. Van Nostrand, Princeton, New Jersey,
1995.
[10] J. Ville, \Theorie et applications de la notion de signal analytique," Cables
et Transmission, vol. 2, pp. 61-74, 1948.
[11] B. Boashash, \Note on the use of the Wigner distribution for time-frequency
signal analysis," IEEE Trans. on Acoust., Speech, and Signal Processing, vol.
33, no.9, pp. 1518-1521, 1988.
[12] F. Hlawatsch and G. F. Boudreaux-Bartels, \Linear and quadratic timefrequency
signal representations," IEEE Signal Processing Mag., pp. 21-67,
April 1992.
[13] S. Oh and R. J. Marks, \Kernel synthesis for generalized time-frequency
distributions using the method of alternating projections onto convex sets,"
IEEE Trans. on Signal Processing, vol. 42, no. 7, pp. 1653-1661, July 1994.
[14] T. A. C. M. Claasen and W. F. G. Mecklenbrauker, \The Wigner
distribution-A tool for time-frequency signal analysis- Part I: Continuous-time
signals," Phillips Jour. of Research., vol. 35, pp. 217-250, 1980.
[15] L. Cohen, \Generalized phase-space distribution function," Jour. Math.
Phys., vol. 7, pp. 781- 786, 1966.
[16] J. Jeong and W. J. Williams, \Kernel design for reduced interference distributions,"
IEEE Trans. on Signal Processing, vol. 40, no. 2, pp. 402-412, Feb.
1992.
[17] T. A. C. M. Claasen and W. F. G. Mecklenbrauker, \The Wigner
distribution-A tool for time-frequency signal analysis- Part III: Relation with
other time-frequency signal transformations," Phillips J. Research., vol. 35,
pp. 372-389, 1980.
[18] T. A. C. M. Claasen and W. F. G. Mecklenbrauker, \The Wigner
distribution-A tool for time-frequency signal analysis- Part II: Discrete-time
signals," Phillips J. Research., vol. 35, pp. 276-300, 1980.
[19] P. Flandrin, \A time-frequency formulation of optimum detection," IEEE
Trans. on Acoust., Speech, and Signal Processing, vol. 36, no. 9, pp. 1377-
1384, Sept. 1988.
[20] J. Jeong and W. J. Williams, \Alias-free generalized discrete-time timefrequency
distributions," IEEE Trans. on Signal Processing, vol. 40, no. 11,
pp. 2757-2765, Nov. 1992.
[21] T. A. C. M. Claasen and W. F. G. Mecklenbrauker, \The aliasing problem
in discrete time Wigner distribution," IEEE Trans. Acoust., Speech, Signal
Processing, vol. 31, pp. 1067-1072, 1983.
[22] P. J. Loughlin, J. W. Pitton and L. E. Atlas, \Bilinear time-frequency representations:
New insights and properties," IEEE Trans. on Signal Processing,
vol. 41, no. 2, pp. 750-767, Feb. 1993.
[23] S. Oh and R. J. Marks, \Some properties of the generalized time frequency
representation with cone-shaped kernel," IEEE Trans. on Signal Processing,
vol. 40, no. 7, pp. 1735-1745, July 1992.
[24] R. N. Czerwinski, Adaptive Time-Frequency Analysis Using a Cone-Shaped
Kernel, M.S. Thesis, University of Illinois at Urbana-Champaign, January
1993.
[25] R. N. Czerwinski and D.L.Jones, \Adaptive cone-kernel time-frequency analysis,"
IEEE Trans. on Signal Processing, vol.43, no. 7, pp. 1715-1719, July
1995.
[26] R. N. Czerwinski and D.L.Jones, \An adaptive time-frequency representation
using a cone-shaped kernel," Proc. IEEE ICASSP-93, vol. 4, pp. 404-407, April
1993.
[27] http://www.espacotalassa.com/
[28] H. L. Van Trees, Detection Estimation and Modulation Theory, Part I, N.Y.,
Wiley, 1986.
[29] G. H. Golub and C. F. Van Loan, Matrix Compution, Johns Hopkins University
Press, 1983.
[30] S. Haykin and B. V. Veen, Signal and systems, Wiley, 1999.
[31] S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory,
1993.
[32] S. Haykin, Communication Systems, 3rd. ed., Wiley, 1994.
[33] Raghavan S. V. and Satish K. Tripathi, \Intergrating MultipleWeb-based geographic
information systems,"IEEE Multimedia, vol. 6, no. 1,pp. 49-61,Jan.-
Mar., 1999.

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