Title :
Modal Trajectory Estimation Using Maximum Gaussian Mixture
Author_Institution :
LAAS, Toulouse, France
Abstract :
This technical note deals with the estimation of the whole trajectory of a stochastic dynamic system with highest probability, conditionally upon the past observation process, using a maximum Gaussian mixture. We first recall the Gaussian sum technique applied to minimum variance filtering. It is then shown that the same concept of Gaussian mixture can be applied in that context, provided we replace the Sum operator by the Max operator.
Keywords :
Gaussian processes; estimation theory; filtering theory; mathematical operators; stochastic processes; Gaussian sum technique; Max operator; Sum operator; maximum Gaussian mixture; minimum variance filltering; modal trajectory estimation; past observation process; stochastic dynamic system whole trajectory; Approximation algorithms; Approximation methods; Bayesian methods; Kalman filters; Noise; Probability density function; Trajectory; Filtering; Gaussian mixture; Gaussian sum; smoothing;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2012.2211439