Particle Learning Methods for State and Parameter Estimation

Nemeth, Christopher and Fearnhead, Paul and Mihaylova, Lyudmila and Vorley, D. (2012) Particle Learning Methods for State and Parameter Estimation. In: Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET :. UNSPECIFIED, GBR. ISBN 978-1-84919-624-6

[thumbnail of IET_2012_Particl_learning_5.1.pdf]
PDF (IET_2012_Particl_learning_5.1.pdf)
IET_2012_Particle_Learning_5.1.pdf - Published Version

Download (323kB)


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.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
?? parameter estimationmonte carlo methodsnonlinear filteringchangepoint detectioncomputing, communications and ictqa75 electronic computers. computer science ??
ID Code:
Deposited By:
Deposited On:
22 May 2012 09:16
Last Modified:
16 Jul 2024 02:54