DocumentCode :
3049162
Title :
Improved Pisarenko´s sinusoidal spectrum estimate via SVD subspace approximation methods
Author :
Sun-Yuan Kung ; Yu Hen Hu
Author_Institution :
University of Southern California, Los Angeles, California
fYear :
1982
fDate :
8-10 Dec. 1982
Firstpage :
1312
Lastpage :
1314
Abstract :
This paper presents two numerically stable Pisarenko type spectrum estimators based on a subspace approximation approach. A sinusoidal signal plus noise model is assumed. By using the singular value decomposition, the covariance matrix is decomposed into a signal subspace which represents the signal component; and a noise subspace which represents the noise contributions. The first method makes use of a signal subspace structure which characterizes the signal covariance matrix by a linear system triple (A, b, c). Then the frequencies of the signal sinusoids are solved as the eigenvalues of the A matrix. The second method utilizes a Toeplitz structure of the noise subspace. Then a subspace approximation procedure is taken to find an estimate of this noise subspace. The frequency estimates are then solved as the roots of the defining sequence of this Toeplitz noise subspace matrix. Simulation results are furnished to illustrate the advantages of these proposed new methods.
Keywords :
Approximation methods; Tellurium;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 1982 21st IEEE Conference on
Conference_Location :
Orlando, FL, USA
Type :
conf
DOI :
10.1109/CDC.1982.268371
Filename :
4047474
Link To Document :
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