Particle learning methods for state and parameter estimation
Christopher Nemeth, Paul Fearnhead, Lyudmila S. Mihaylova, D. Vorley · 2012
This paper presents an approach for online parameter estimation within particle filters. Current research has mainly been focused towards the estimation of static parameters. However, in scenarios of target maneuverability, it is often necessary to update the parameters of the model to meet the changing conditions of the target. The novel aspect of the proposed approach lies in the estimation of non-static parameters which change at some unknown point in time. Our parameter estimation is updated using changepoint analysis, where a changepoint is identified when a significant change occurs in the observations of the system, such as changes in direction or velocity.