Title :
On estimating noncausal nonminimum phase ARMA models of non-Gaussian processes
Author :
Giannakis, Georgios B. ; Swami, Ananthram
Author_Institution :
Dept. of Electr. Eng., Virginia Univ., Charlottesville, VA, USA
fDate :
3/1/1990 12:00:00 AM
Abstract :
The authors address the problem of estimating the parameters of non-Gaussian ARMA (autoregressive moving-average) processes using only the cumulants of the noisy observation. The measurement noise is allowed to be colored Gaussian or independent and identically non-Gaussian distributed. The ARMA model is not restricted to be causal or minimum phase and may even contain all-pass factors. The unique parameter estimates of both the MA and AR parts are obtained by linear equations. The structure of the proposed algorithm facilitates asymptotic performance evaluation of the parameter estimators and model order selection using cumulant statistics. The method is computationally simple and can be viewed as the least-squares solution to a quadratic model fitting of a sampled cumulant sequence. Identifiability issues are addressed. Simulations are presented to illustrate the proposed algorithm
Keywords :
least squares approximations; parameter estimation; random noise; signal processing; all-pass factors; asymptotic performance evaluation; autoregressive moving-average; colored Gaussian; cumulant statistics; least-squares solution; linear equations; measurement noise; model order selection; noisy observation cumulants; nonGaussian ARMA processes; noncausal nonminimum phase ARMA models; parameter estimates; quadratic model fitting; sampled cumulant sequence; signal processing; Autocorrelation; Fading; Gaussian noise; Noise measurement; Parameter estimation; Phase estimation; Pollution measurement; Signal processing; Signal processing algorithms; Sonar detection;
Journal_Title :
Acoustics, Speech and Signal Processing, IEEE Transactions on