Particle learning methods for state and parameter estimation

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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.

Citation

Nemeth, C., Fearnhead, P., Mihaylova, L. and Vorley, D., 2012, May. Particle learning methods for state and parameter estimation. In 9th IET Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications (pp. 1-6). IET.

@inproceedings{nemeth2012particle,
  title={Particle learning methods for state and parameter estimation},
  author={Nemeth, Christopher and Fearnhead, Paul and Mihaylova, Lyudmila and Vorley, Dave},
  booktitle={9th IET Data Fusion \& Target Tracking Conference (DF\&TT 2012): Algorithms \& Applications},
  pages={1--6},
  year={2012},
  organization={IET}
}