Title :
A modified Gaussian sum approach to estimation of non-Gaussian signals
Author :
Caputi, Mauro J. ; Moose, Richard L.
Author_Institution :
Dept. of Eng., Hofstra Univ., Hempstead, NY, USA
fDate :
4/1/1993 12:00:00 AM
Abstract :
A Gaussian sum estimation algorithm has previously been developed to deal with noise processes that are non-Gaussian. Inherent in this algorithm is a serious growing memory problem that causes the number of terms in the Gaussian sum to increase exponentially at each iteration. A modified Gaussian sum estimation algorithm using an adaptive filter is developed that avoids the growing memory problem of the previous algorithm while providing effective state estimation. The adaptive filter is comprised of a fixed set of estimators operating in parallel with each individual estimate possessing its own corresponding weighting term. A simulation example illustrates the new non-Gaussian estimation technique
Keywords :
Kalman filters; adaptive filters; digital filters; digital simulation; noise; parallel processing; signal processing; state estimation; Kalman filter; adaptive filter; estimation algorithm; memory; modified Gaussian sum; noise; nonGaussian noise; simulation; state estimation; Adaptive filters; Approximation algorithms; Density functional theory; Density measurement; Gaussian noise; Markov processes; Probability; Random processes; Random variables; Signal processing; State estimation;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on