Title :
Non-Gaussian multivariate adaptive AR estimation using the super exponential algorithm
Author :
Martone, Massimiliano
Author_Institution :
Telecomm. Group, Watkins-Johnson Co., Gaithersburg, MD, USA
fDate :
10/1/1996 12:00:00 AM
Abstract :
We formulate as a deconvolution problem the causal/noncausal non-Gaussian multichannel autoregressive (AR) parameter estimation problem. The super exponential algorithm presented in a paper by Shalvi and Weinstein (1993) is generalized to the vector case. We present an adaptive implementation that is very attractive since it is higher order statistics (HOS) based but does not present the high computational complexity of methods proposed up to now
Keywords :
adaptive estimation; autoregressive processes; computational complexity; deconvolution; higher order statistics; iterative methods; vectors; adaptive implementation; computational complexity; deconvolution problem; higher order statistics; multichannel autoregressive parameter estimation; nonGaussian multivariate adaptive AR estimation; super exponential algorithm; vector case; Adaptive signal processing; Channel estimation; Deconvolution; Equations; Higher order statistics; Image analysis; Parameter estimation; Signal analysis; Signal processing algorithms; Time series analysis;
Journal_Title :
Signal Processing, IEEE Transactions on
