Title :
Application of an improved particle filter for state estimation
Author :
Li, Xiang ; Yu, Liu ; Baoku, Su
Author_Institution :
Space Control&Inertial Technol. Res. Center, Harbin Inst. of Technol., Harbin
Abstract :
A novel Gaussian mixture sigma-point particle filter algorithm is proposed to mitigate the sample depletion problem. The posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. The simulation results demonstrate the validity of the proposed algorithm.
Keywords :
Gaussian processes; Monte Carlo methods; expectation-maximisation algorithm; particle filtering (numerical methods); state estimation; Gaussian mixture model; Gaussian mixture sigma-point particle filter algorithm; measurement update; posterior state density; sample depletion problem; state estimation; weighted expectation-maximization algorithm; weighted particle set; Density measurement; Filtering algorithms; Monte Carlo methods; Nonlinear dynamical systems; Nonlinear equations; Particle filters; Particle measurements; Space technology; State estimation; Vehicle dynamics; Bearing only tracking; Estimation algorithm; Monte carlo simulation; Particle filter;
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
DOI :
10.1109/CHICC.2008.4604962
