DocumentCode :
2030595
Title :
Asymptotical analysis of MUSIC and ESPRIT frequency estimates
Author :
Eriksson, Anders ; Stoica, Petre ; Söderström, Torsten
Author_Institution :
Syst. & Control Group, Uppsala Univ., Sweden
Volume :
4
fYear :
1993
fDate :
27-30 April 1993
Firstpage :
556
Abstract :
The authors present expressions for the variance of the multiple signal classification (MUSIC) and ESPRIT frequency estimates derived under the assumption that the sample covariance matrix is close to its asymptotical value. This assumption is valid for a sufficiently high signal-to-noise ratio, but also for a large number of data samples. It is shown that the expressions derived here encompass both the high SNR analysis presented earlier and the large sample analysis described by P. Stoica and T. Soderstrom (IEEE Trans. vol.SP-39, no.8, p.1836-47, Aug. 1991). The theoretical results are supported by the results obtained from Monte Carlo simulations.<>
Keywords :
Monte Carlo methods; array signal processing; parameter estimation; variational techniques; ESPRIT frequency estimates; MUSIC; Monte Carlo simulations; asymptotical analysis; multiple signal classification; sample covariance matrix;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on
Conference_Location :
Minneapolis, MN, USA
ISSN :
1520-6149
Print_ISBN :
0-7803-7402-9
Type :
conf
DOI :
10.1109/ICASSP.1993.319718
Filename :
319718
Link To Document :
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