Nonlinear Filtering of Convex Sets of Probability Distributions

A solution is provided to the problem of computing a convex set of conditional probability distributions that characterize the state of a nonlinear dynamic system as it evolves in time. The estimator uses the Galerkin approximation to solve Kolmogorov's equation for the di usion of a continuous-time nonlinear system with discrete-time measurement updates. Filtering of the state is accomplished for a convex set of distributions simultaneously, and closed-form representations of the resulting sets of means and covariances are generated. Keywords. nonlinear ltering theory, convex sets of probability distributions, set-valued estimation

• Type: Conference
• Year: 1999
• Where: ISIPTA
• Authors: John Kenney, Wynn C. Stirling
• Keywords: Conditional Probability Distributions, Convex Sets, ISIPTA 1999, Mathematics, Probability Distributions