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Particle Learning Methods for State and Parameter Estimation

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date15/05/2012
Host publicationData Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET
Number of pages6
ISBN (Electronic)978-1-84919-624-6
Original languageEnglish

Conference

ConferenceThe 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications
CountryUnited Kingdom
Period16/05/1217/05/12

Conference

ConferenceThe 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications
CountryUnited Kingdom
Period16/05/1217/05/12

Abstract

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.