A subspace approach to estimation of autoregressive parameters fromnoisy measurements

Authors:
C.E. Davila
Affiliations:
Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX
Venue:
IEEE Transactions on Signal Processing
Year:
1998

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Abstract

This correspondence describes a method for estimating the parameters of an autoregressive (AR) process from a finite number of noisy measurements. The method uses a modified set of Yule-Walker (YW) equations that lead to a quadratic eigenvalue problem that, when solved, gives estimates of the AR parameters and the measurement noise variance