Asymptotical analysis of MUSIC and ESPRIT frequency estimates

Authors:
A. Eriksson;P. Stoica;T. Soderstrom
Affiliations:
Syst. & Control Group, Uppsala Univ., Sweden;Syst. & Control Group, Uppsala Univ., Sweden;Syst. & Control Group, Uppsala Univ., Sweden
Venue:
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Year:
1993

Citing 1
Cited 1

High-SNR asymptotics for signal-subspace methods in sinusoidalfrequency estimation

IEEE Transactions on Signal Processing

Sinusoidal order estimation using angles between subspaces

EURASIP Journal on Advances in Signal Processing

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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.