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Subspace-based parameter estimation of symmetric noncausal autoregressive signals from noisy measurements

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2 Author(s)
P. Stoica ; Syst. & Control Group, Uppsala Univ., Sweden ; J. Sorelius

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

Published in:

IEEE Transactions on Signal Processing  (Volume:47 ,  Issue: 2 )