Particle learning methods for state and parameter estimation


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 maneuver-ability, 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 change point analysis, where a change point is identified when a significant change occurs in the observations of the system, such as changes in direction or velocity.

9th IET Data Fusion and Target Tracking Conference, London, U.K.