DocumentCode :
3070394
Title :
Application of SVD to 2-D spectral estimation
Author :
Miao, Nan ; Chen, Zong-Zhi
Author_Institution :
Institute of Aeronautics & Astronautics, Beijing, China
Volume :
9
fYear :
1984
fDate :
30742
Firstpage :
142
Lastpage :
145
Abstract :
In this paper, a new method by which some modern techniques of one dimension (1-D) spectral estimation can be extended to two dimension (2-D) cases is presented. The main point of the new method is to find the optimum separable approximation of a given autocorrelation matrix in the least square sense, so that 2-D spectral estimation can be reduced to 1-D problem. It is proved in this paper that finding the optimum separable approximation of a matrix in the least square sense is equivalent to finding separable representation of the matrix by singular value decomposition (SVD). Finally, some results of experiments are shown to illustrate the performance of the new method and to compare with other 2-D spectral estimation methods.
Keywords :
Autocorrelation; Closed-form solution; Eigenvalues and eigenfunctions; Entropy; Iterative methods; Kernel; Least squares approximation; Matrix decomposition; Singular value decomposition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '84.
Type :
conf
DOI :
10.1109/ICASSP.1984.1172368
Filename :
1172368
Link To Document :
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