DocumentCode :
1465045
Title :
Subspace-based parameter estimation of symmetric noncausal autoregressive signals from noisy measurements
Author :
Stoica, Petre ; Sorelius, Joakim
Author_Institution :
Syst. & Control Group, Uppsala Univ., Sweden
Volume :
47
Issue :
2
fYear :
1999
fDate :
2/1/1999 12:00:00 AM
Firstpage :
321
Lastpage :
331
Abstract :
Symmetric noncausal auto-regressive signals (SNARS) arise in several, mostly spatial, signal processing applications. We introduce a subspace fitting approach for parameter estimation of SNARS from noise-corrupted measurements. We show that the subspaces associated with a Hankel matrix built from the data covariances contain enough information to determine the signal parameters in a consistent manner. Based on this result, we propose a multiple signal classification (MUSIC)-like methodology for parameter estimation of SNARS. Compared with the methods previously proposed for SNARS parameter estimation, our SNARS-MUSIC approach is expected to possess a better tradeoff between computational and statistical performances
Keywords :
autoregressive processes; noise; parameter estimation; parameter space methods; signal classification; Hankel matrix; SNARS-MUSIC approach; computational performance; data covariances; multiple signal classification; noise-corrupted measurements; noisy measurements; signal parameters; spatial signal processing; statistical performance; subspace fitting; subspace-based parameter estimation; symmetric noncausal autoregressive signals; Covariance matrix; Degradation; Extraterrestrial measurements; Multidimensional signal processing; Multiple signal classification; Noise measurement; Parameter estimation; Signal processing; Symmetric matrices; White noise;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.740115
Filename :
740115
Link To Document :
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