A unified approach to three eigendecomposition methods for frequency estimation

The authors present a unified approach to three eigendecomposition-based methods for frequency estimation in the presence of noise. These are the Tufts-Kumaresan (TK) method, the minimum-norm (MN) method, and the total least squares (TLS) method. It is shown that: (1) the MN method is a modified ver...

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Bibliographic Details
Published inIEEE transactions on signal processing Vol. 40; no. 1; pp. 213 - 218
Main Authors Banjanin, Z., Cruz, J.R., Zrnic, D.S.
Format Journal Article
LanguageEnglish
Published IEEE 01.01.1992
Subjects
Acoustic signal detection
Estimation theory
Frequency estimation
Gaussian noise
Sensor systems
Signal detection
Signal processing
Solid modeling
Speech processing
Stochastic processes
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Summary:The authors present a unified approach to three eigendecomposition-based methods for frequency estimation in the presence of noise. These are the Tufts-Kumaresan (TK) method, the minimum-norm (MN) method, and the total least squares (TLS) method. It is shown that: (1) the MN method is a modified version of the TK method; (2) the TLS method is a generalization of the MN method; (3) the TLS solution vector can be expressed in matrix form, and an alternative way of computing it is presented; (4) the MN and the TLS methods exhibit some improvement over the TK method.< >
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1053-587X
1941-0476
DOI:10.1109/78.157196