跳到主要內容

臺灣博碩士論文加值系統

(44.210.85.190) 您好!臺灣時間:2022/12/10 14:36
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:蘇順吉
研究生(外文):Shun-Chi Su
論文名稱:水下穩態與暫態音響信號頻譜特徵之研究
論文名稱(外文):Studies on underwater acoustic stationary and transient signals spectrum features
指導教授:徐 學 群
指導教授(外文):Hsuen-Chyun Shyu
學位類別:碩士
校院名稱:中正理工學院
系所名稱:電機工程研究所
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:1998
畢業學年度:86
語文別:中文
論文頁數:95
中文關鍵詞:基波轉換傅利葉轉換多重尺度基波多重轉移基波
外文關鍵詞:wavelet transformFourier transformmulti-scaling waveletmulti-translation wavelet
相關次數:
  • 被引用被引用:0
  • 點閱點閱:162
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
水下音響信號因受各種主、客觀因素的影響,而具有非線性及時變的特性,且通常信號雜訊比非常地低,使得在分析處理上極為複雜與困難。本論文針對水下音響中的穩態及暫態信號,提出實際並且可行的理論與處理方法,並嚐試設計出有效率的分析辨識系統,對所蒐集的水下音響信號進行分析測試。
對於穩態信號頻譜而言,利用改良的功率頻譜密度函數求其穩態頻譜,再利用正規化的方法,去除背景雜訊,最後,萃取出頻譜上的主要特徵值,以減少資料量,加速後續的分析辨識運算。
而就暫態信號頻譜方面,其處理上較為困難,基波轉換是目前公認比較好的方法,但由於水下音響信號的變異成分相當多,因此,本論文提出了更具成效的多重尺度與多重轉移基波轉換,來完整地獲取暫態信號頻譜特徵。
藉由所提出的分析演算法則,我們進一步利用個人電腦,在Matlab的環境下,開發出水下音響信號分析與辨識系統的雛形,以驗證所提方法之可行性與正確性,並希望能逐步建立自動化之水下目標偵測系統。
Underwater acoustic signals are non-linear, time-varying, and with low signal-to-noise ratio. These properties make the signal analysis difficulty and complex. For resolving targets through the underwater acoustic signals, effective methods are proposed in this thesis to process underwater acoustic signals, Base on these methods, an signal acoustic recognition system is also designed.
Traditionally, the Fourier transform (FT) and Morlet wavelet transform (MWT) are the main tool for stationary and transient signals spectrum analysis, respectively. Here in, a modify power spectrum density (PSD) function is used to extract the critical features for stationary underwater acoustic signals, A multi-scaling MWT kernel is also proposed which can depict the underwater transient spectrum successfully.
To illustrate the effectiveness of these two novel design methods, some experiments are taken to perform by using simulation and recorded real underwater acoustic signals. Experimented results show that the proposed methods can detect and analyze both stationary and transient underwater acoustic signals successfully. An underwater acoustic signals analysis is also implemented on Matlab base personal computer to detect, analyze, and recognize targets by stationary signal features. It is hoped that an automatic underwater targets recognition system can be realized by methods discussed in this thesis in the future.
第一章 前言
第二章 穩態信號頻譜估計理論與應用
2.1.信號特徵量的估計
2.2.資料前級處理
2.2.1.去除平均值
2.3.特徵量可靠性的評估
2.3.1.估計值的偏差
2.3.2.估計值的方差
2.3.3.有效估計
2.3.4.一致估計
2.4.穩態信號頻譜的估計
2.4.1.相關函數
2.4.2.功率頻譜密度的相關法
2.4.3.週期圖法
2.4.4.Welch法
2.5.寬頻背景雜訊的正規化
2.5.1.正規化的方法
2.6.凝聚函數
2.7.範例說明
第三章 暫態信號頻譜估計理論與應用
3.1. Morlet基波轉換理論
3.1.1. WT基底函數之共同特質
3.1.2. WT之投影空間
3.2.暫態信號頻譜的估計
3.3.多重尺度之MWT
3.3.1.多重尺度MWT的基底函數
3.4.多重轉移之MWT
3.5.範例說明
第四章 水下音響信號處理分析
4.1.水下音響信號處理的環境因素
4.1.1.輸入信號的特性
4.1.2.雜訊及干擾的型態
4.1.3.與海水介質通道的互動關係
4.2.水下音響信號原始資料檔案
4.3.穩態信號頻譜特徵分析
4.4.暫態信號頻譜特徵分析
第五章 軟體操作介面實作
5.1.軟體發展環境
5.2.Matlab之圖形使用者介面(GUI)
5.3.軟體操作介面之架構
5.4.軟體操作介面之功能
5.4.1.水下音響信號分析操作介面
5.4.2.頻譜分析操作介面
5.4.3.資料庫編輯操作介面
第六章 結論
參考文獻
附錄
1. G. Bark, On the mechanisms of propeller cavitation noise., Chalmers University of Technology, Sweden, 1988.
2. J. C. Hassab, Underwater signal and data processing., CRC Press. Inc., U. S. A., 1989.
3. Urick, Robert J., Principles of Underwater Sound/3rd Edition., McGraw-Hill, Inc., pp. 202-205, pp. 328-341, 1983.
4. S. Bochner and K. Chandrasekharan, Fourier Transforms., Princeton Univ. Press, pp.1-159, 1948.
5. E. C. Titchmarsh, Introduction to the Theory of Fourier Integrals. Oxford Univ., Press , pp.212-242, 1950.
6. N. G. DeBruijn, Uncertainty principles in Fourier analysis in Inequalities., O. Shisha (Ed.), Academic Press, New York, pp.57-71, 1967.
7. M. Desal, and D. Shazeer, "Acoustic transient analysis using wavelet decomposition," IEEE Proc. Conf. on N.N.O.E. 3064-3, pp.29-40, 1991.
8. M. Frisch, and H. Messer, "The use of the wavelet transform in the detection of an unknown transient signal," IEEE Trans. Inf. Theory, 38-2, pt II Special Iss., pp.892-897, 1992.
9. K. V. Bury, Statistical models in applied science., John Wiley & Sons, Inc., 1975.
10. A. Papoulis, Signal Analysis., McGraw-Hill, New York, NY, 1977.
11. F. J. Harris, "On the use of Windows for Harmonic Analysis with the Discrete Fourier Transform ," IEEE Proc., 66, pp.51-83, 1978.
12. J. Wolcin, "on the statistical properties of noise background equalization schemes," NCSC Tech. Memo. No. 781159, Naval Underwater Systems Center, New London, CT 31 July, 1978.
13. W. Struzinski, " A new normalization algorithm for detection systems," J. Acoust. Soc. Am. Suppl. 175, S43, 1984.
14. William A. Struzinski and Edward D. Lowe, "A performance comparison of four noise background normalization schemes proposed for signal detection systems," J. Acoust. Soc. Am. 76(6), Dec., 1984.
15. G. V. Trunk, "Detection results for scanning Radars employing feedback integration," IEEE Trans. on Aerospace and Electronic System, AES-6, pp.522-527, 1970.
16. L. Cohen, "Time-frequency distribution - A review," IEEE Proc. 77-7, pp.941-981, 1989.
17. C. K. Chui, An Introduction to Wavelets., Academic Press Inc. Texas, pp.49-74, 215-240, 1992.
18. Y. T. Chan, Wavelet Basics., Kluwer Academic Publishers, Massachusetts, pp.1-49, 1995.
19. William A. Gardner, Statistical Spectral Analysis., Prentice Hall, pp.3-27, 34-104,179-240, 1988
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top