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