Title :
A subspace approach to estimation of autoregressive parameters from noisy measurements
Author :
Davila, Carlos E.
Author_Institution :
Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA
fDate :
2/1/1998 12:00:00 AM
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
Keywords :
autoregressive processes; eigenvalues and eigenfunctions; parameter estimation; random noise; random processes; spectral analysis; AR process; Yule-Walker equations; autoregressive process; measurement noise variance; noisy measurements; parameter estimation; quadratic eigenvalue problem; subspace approach; Additive noise; Autocorrelation; Biomedical measurements; Eigenvalues and eigenfunctions; Equations; Linear predictive coding; Noise measurement; Parameter estimation; Random processes; White noise;
Journal_Title :
Signal Processing, IEEE Transactions on
