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Particle Learning Methods for State and Parameter Estimation

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This paper presents an approach for online parameter estimation within particle lters. 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 changepoint analysis, where a changepoint is identied when a signicant change occurs in the observations of the system, such as changes in direction or velocity.