Particle learning methods for state and parameter estimation

Published in 9th IET Data Fusion and Target Tracking Conference, 2012

Recommended citation: Nemeth, C., Fearnhead, P., Mihaylova, L. and Vorley, D. (2012). "Bearings-only tracking with particle filtering for joint parameter learning and state estimation," 15th International Conference on Information Fusion, pp. 824-831.

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.