DocumentCode :
3018670
Title :
Super-Fast algorithm for minimum variance (Capon) spectral estimation
Author :
Marple, S. Lawrence, Jr. ; Adeli, Majid ; Liu, Huaping
Author_Institution :
Sensors & Electromagn. Applic. Lab., Georgia Tech Res. Inst., Smyrna, GA, USA
fYear :
2010
fDate :
7-10 Nov. 2010
Firstpage :
1832
Lastpage :
1836
Abstract :
The minimum variance spectral estimator (sometimes referenced as the Capon spectral estimator, or the minimum variance distortion-less response estimator) is a high resolution spectral estimator used extensively in practice as an alternative to the classical squared-magnitude Fourier estimator. In its original form without special algorithms, the one-dimensional formulation requires order p3 computations, in which p is the minimum variance filter size. The current implementation used in practice employs a fast algorithm developed by Musicus, requiring order p2 + p log2(2p) computations. This paper shows discoveries of additional exploitable structure to bring the computational complexity down to p + p log2(p), creating a superfast algorithm. Furthermore, by exploiting a differential relationship between two quantities in this structure, an approximated version of the proposed algorithm with an even lower computational complexity is derived.
Keywords :
Fourier analysis; computational complexity; maximum likelihood estimation; spectral analysis; Capon spectral estimator; classical squared-magnitude Fourier estimator; computational complexity; minimum variance filter size; minimum variance spectral estimation; super-fast algorithm; Algorithm design and analysis; Approximation algorithms; Approximation methods; Computational complexity; Filtering algorithms; Signal processing algorithms; Minimum variance spectral estimation; computational complexity; linear prediction; maximum likelihood spectral estimation; spectral analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signals, Systems and Computers (ASILOMAR), 2010 Conference Record of the Forty Fourth Asilomar Conference on
Conference_Location :
Pacific Grove, CA
ISSN :
1058-6393
Print_ISBN :
978-1-4244-9722-5
Type :
conf
DOI :
10.1109/ACSSC.2010.5757893
Filename :
5757893
Link To Document :
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