Field Kalman Filter and its approximation
Piotr Bania, Jerzy Baranowski · 2016
Estimation of state, parameter and disturbances in dynamical system is a consistently investigated issue world-wide. In this paper we propose the Field Kalman Filter - a method for realization of this task in linear stochastic systems. In particular we prove, using Bayes' theorem, the formulas describing probability distributions of investigated quantities. Additionally we give an approximate implementation based on moving horizon approach. Efficiency of the method is illustrated with a particle tracking problem and by comparing it to competitive approaches - ALS and direct Bayesian estimation.