Zoom ID: 868 2868 1159


The problem of optimal state estimation is to determine the best estimate of the solution history of a dynamical system given some partial and inaccurate measurements. The filtering problem is defined as that of estimating the present state given prior observations. It is generally accepted that the optimal solution is obtained by calculating the conditional statistical distribution of the state vector of the system given the set of measurements up to the current time. Between measurements, the conditional probability density solves the forward Kolmogorov equation, a parabolic partial differential equation for the state space. At measurement times, the probability density function is updated by Bayes’s rule. In this talk, I will discuss mathematical approaches to the Bayesian filtering problem and show how to obtain such optimal states with examples.