Instantaneous Frequency Estimation with Modified Trench's Method

碩士 === 國立中山大學 === 電機工程研究所 === 82 === The problem of estimating the frequency content of signals is very important in many digital signal processing applications. In this thesis, we are concerned with the problem of estimat- ing and trackin...

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Bibliographic Details
Main Authors: Tsai, Lin Chung, 蔡林忠
Other Authors: Chern, Shiunn Jang
Format: Others
Language:en_US
Published: 1994
Online Access:http://ndltd.ncl.edu.tw/handle/46294972300596587372
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Summary:碩士 === 國立中山大學 === 電機工程研究所 === 82 === The problem of estimating the frequency content of signals is very important in many digital signal processing applications. In this thesis, we are concerned with the problem of estimat- ing and tracking the instantaneous frequency of the sinusoidal signal together with additive white noise. Its solution has im- portant applications in the fields of vibration measurements, Doppler radar returns, passive sonar systems, and formant frequency estimation of speech signals. In this thesis, a new algorithm for IFE is developed. To do so, the forward linear prediction filter is employed. In consequence, the modified Trench's method along with the Bauer-Fike theorem is proposed for solving the principal eigenvalues of the Hermitian Toepli- tz autocorrelation matrix for instantaneous frequency estima- tion (IFE).In fact,three kinds of eigenvalue searching schemes can be employed in the modified Trench's method. In the new algorithm for IFE, the modified Trench's method is first used for solving the principal eigenvalues for initial block of data with length N. When a new data is received,the Bauer-Fike theorem is applied to search the new eigenvalues based on the previous obtained eigenvalues.Such that the computational cost can be reduced. The performance of the IFE using the presented methodis compared with the conventional LMS adaptive method as well as the QR based method. From the simulation results, we found that the presented method can perform as good as the QR based method, in terms of multiple frequencies estimation where the frequencies are closer.But in the same situation the conventional LMS adaptive method may not perform satisfacto- rily. Moreover, the computational complexity of the presented method is much less than QR based method, especially when the presented method is implemented by the parallelized structure.