Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Speech enhancement as a realisation issue
Signal Processing - Signal processing with heavy-tailed models
Parameter estimation of multichannel autoregressive processes in noise
Signal Processing
Signal Processing - Fractional calculus applications in signals and systems
Information science: Properties of infinite covariance matrices and stability of optimum predictors
Information Sciences: an International Journal
Adaptive AR modeling in white Gaussian noise
IEEE Transactions on Signal Processing
A subspace approach to estimation of autoregressive parameters fromnoisy measurements
IEEE Transactions on Signal Processing
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Autoregressive (AR) models are used in a wide variety of applications concerning the recovery of signals from noise-corrupted observations. In all real contexts of this kind also an additive broadband observation noise is present and the filtering of the observations is usually performed by means of standard Kalman filtering that requires a state space realization of the AR model to describe the observed process and the solution, at every step, of the Riccati equation. This paper proposes a faster filtering algorithm suitable for stationary processes and based on the decomposition of Toeplitz matrices described in [J. Rissanen, Algorithms for triangular decomposition of block Hankel and Toeplitz matrices with application to factoring positive matrix polynomials, Math. Comput. 27 (January 1973) 147-154] that operates directly on AR models. The computational complexity of the proposed algorithm increases only linearly with the order of the process.