12,000
We have over 12,000 students, from over 100 countries, within one of the safest campuses in the UK
93%
93% of Lancaster students go into work or further study within six months of graduating
View graph of relations
Download
Particle Learning Methods for State and Paramet...
316 KB, PDF-document
18/05/12
Text available via DOI:
Particle Learning Methods for State and Parameter Estimation
Research output: Contribution in Book/Report/Proceedings › Paper
Published
Associated organisations
| Publication date | 15/05/2012 |
|---|---|
| Host publication | Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-84919-624-6 |
| Original language | English |
Conference
| Conference | The 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications |
|---|---|
| Country | United Kingdom |
| Period | 16/05/12 → 17/05/12 |
Conference
| Conference | The 9th IET Data Fusion & Target Tracking Conference (DF & TT'2012). Algorithms & Applications |
|---|---|
| Country | United Kingdom |
| Period | 16/05/12 → 17/05/12 |
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.
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.