Event-Based State Estimation Using the Auxiliary Particle Filter
Ruuskanen, Johan LU
and Cervin, Anton
LU
(2019)
2019 European Control Conference
- Abstract
- Event-based sampling provides a way of lowering the resource utilization in sensing and communication applications. By sending a sample only when some triggering condition is fulfilled, we can ensure that the transmitted samples actually carry innovation. However, in an event-based system, the state estimation problem becomes complicated, as the information of not receiving a measurement must be taken into consideration. Recent research has examined the feasibility of using particle filters for solving the event-based state estimation problem. To the best of our knowledge, only the simple bootstrap particle filter has so far been considered in this setting. We argue that, as this filter does not fully utilize the current measurement, it is... (More)
- Event-based sampling provides a way of lowering the resource utilization in sensing and communication applications. By sending a sample only when some triggering condition is fulfilled, we can ensure that the transmitted samples actually carry innovation. However, in an event-based system, the state estimation problem becomes complicated, as the information of not receiving a measurement must be taken into consideration. Recent research has examined the feasibility of using particle filters for solving the event-based state estimation problem. To the best of our knowledge, only the simple bootstrap particle filter has so far been considered in this setting. We argue that, as this filter does not fully utilize the current measurement, it is not well suited for state estimation in event-based systems.
We propose an extension to the auxiliary particle filter for systems with event-based measurements, in which certain existing techniques for finding an approximation of the fully adapted filter can easily be utilized. In a simulation study, we demonstrate that at new measurement events, the benefits of using the auxiliary particle filter increases when fewer measurements are being sent. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ce129da1-b3e4-4f8e-81fc-2fa8747d9fcc
- author
- Ruuskanen, Johan
LU
and Cervin, Anton
LU
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2019 European Control Conference (ECC)
- publisher
- IEEE Press
- conference name
- 2019 European Control Conference
- conference location
- Napoli, Italy
- conference dates
- 2019-06-25 - 2019-06-28
- external identifiers
-
- scopus:85071586107
- ISBN
- 978-3-907144-00-8
- DOI
- 10.23919/ECC.2019.8796091
- project
- Event-Based Estimation and Control
- Event-Based Information Fusion for the Self-Adaptive Cloud
- language
- English
- LU publication?
- yes
- id
- ce129da1-b3e4-4f8e-81fc-2fa8747d9fcc
- date added to LUP
- 2019-06-11 14:32:24
- date last changed
- 2022-05-03 21:57:09
@inproceedings{ce129da1-b3e4-4f8e-81fc-2fa8747d9fcc,
abstract = {{Event-based sampling provides a way of lowering the resource utilization in sensing and communication applications. By sending a sample only when some triggering condition is fulfilled, we can ensure that the transmitted samples actually carry innovation. However, in an event-based system, the state estimation problem becomes complicated, as the information of not receiving a measurement must be taken into consideration. Recent research has examined the feasibility of using particle filters for solving the event-based state estimation problem. To the best of our knowledge, only the simple bootstrap particle filter has so far been considered in this setting. We argue that, as this filter does not fully utilize the current measurement, it is not well suited for state estimation in event-based systems.<br/>We propose an extension to the auxiliary particle filter for systems with event-based measurements, in which certain existing techniques for finding an approximation of the fully adapted filter can easily be utilized. In a simulation study, we demonstrate that at new measurement events, the benefits of using the auxiliary particle filter increases when fewer measurements are being sent.}},
author = {{Ruuskanen, Johan and Cervin, Anton}},
booktitle = {{2019 European Control Conference (ECC)}},
isbn = {{978-3-907144-00-8}},
language = {{eng}},
publisher = {{IEEE Press}},
title = {{Event-Based State Estimation Using the Auxiliary Particle Filter}},
url = {{https://lup.lub.lu.se/search/files/65832881/ecc19.pdf}},
doi = {{10.23919/ECC.2019.8796091}},
year = {{2019}},
}