Title :
Multivariate ARMA modeling by scalar algorithms
Author :
Chakraborty, Mrityunjoy ; Prasad, Surendra
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol., New Delhi, India
fDate :
4/1/1993 12:00:00 AM
Abstract :
An algorithm for multichannel autoregressive moving average (ARMA) modeling which uses scalar computations only and is well suited for parallel implementation is proposed. The given ARMA process is converted to an equivalent scalar, periodic ARMA process. The scalar autoregressive (AR) parameters are estimated by first deriving a set of modified Yule-Walker-type equations and then solving them by a parallel, order recursive algorithm. The moving average (MA) parameters are estimated by a least squares method from the estimates of the input samples obtained via a high-order, periodic AR approximation of the scalar process
Keywords :
least squares approximations; parameter estimation; signal processing; statistical analysis; time series; ARMA process; Yule-Walker-type equations; least squares method; multichannel autoregressive moving average; multivariate ARMA modelling; parallel implementation; parameter estimation; periodic AR approximation; scalar algorithms; scalar autoregressive parameters; scalar computations; Acoustic noise; Chromium; Costs; Least squares methods; Maximum likelihood estimation; Parameter estimation; Resonance; Signal processing algorithms; Spectral analysis; Underwater acoustics;
Journal_Title :
Signal Processing, IEEE Transactions on
