Title :
PGF 42: Progressive Gaussian filtering with a twist
Author :
Hanebeck, Uwe D.
Author_Institution :
Intell. Sensor-Actuator-Syst. Lab. (ISAS), Karlsruhe Inst. of Technol. (KIT), Karlsruhe, Germany
Abstract :
A new Gaussian filter for estimating the state of nonlinear systems is derived that relies on two main ingredients: i) the progressive inclusion of the measurement information and ii) a tight coupling between a Gaussian density and its deterministic Dirac mixture approximation. No second Gaussian assumption for the joint density of state and measurement is required, so that the performance is much better than that of Linear Regression Kalman Filters (LRKFs), which heavily rely on this assumption. In addition, the new filter directly works with the generative system description. No Likelihood function is required. It can be used as a plug-in replacement for standard Gaussian filters such as the UKF.
Keywords :
Gaussian processes; filtering theory; nonlinear filters; nonlinear systems; state estimation; Gaussian density; LRKFs; PGF 42; UKF; deterministic Dirac mixture approximation; generative system description; high-dimensional nonlinear systems; joint state density; linear regression Kalman filters; measurement information; progressive Gaussian filtering; state estimation; Approximation methods; Covariance matrices; Density measurement; Equations; Mathematical model; Noise; Noise measurement; Dirac mixture approximation; Gaussian filter; homotopy; nonlinear Bayesian state estimation; nonlinear filtering; progressive Bayesian estimation; recursive estimation;
Conference_Titel :
Information Fusion (FUSION), 2013 16th International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-605-86311-1-3
