Effective Parametric Estimation of Non-Gaussian Autoregressive Moving Average Processes Exhibiting Noise with Impulses

Authors:
Preston D. Frazier;M. F. Chouikha
Affiliations:
General Dynamics Corporation, Hanover, USA 21076;Department of Electrical and Computer Engineering, Howard University, Washington, USA 20059
Venue:
Journal of VLSI Signal Processing Systems
Year:
2006

Citing 4
Cited 0

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Abstract

In statistical signal processing, parametric modeling of non-Gaussian processes experiencing noise interference is a very important research topic. Particularly challenging to some researchers is how to estimate signals encountering stochastic noise process exhibiting sharp spikes. The authors propose the use of systems with impulse effect along with the classic autoregressive moving average model as a novel parametric modeling tool to successfully estimate these specific processes. The proficiency of this original system is illustrated in a performance table.