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On Innovation-Based Triggering for Event-Based Nonlinear State Estimation Using the Particle Filter

Ruuskanen, Johan LU orcid and Cervin, Anton LU orcid (2020) European Control Conference (ECC 20)
Abstract
Event-based sampling has been proposed as a general technique for lowering the average communication rate, energy consumption and computational burden in remote state estimation. However, the design of the event trigger is critical for good performance. In this paper, we study the combination of innovation-based triggering and state estimation of nonlinear dynamical systems using the particle filter. It is found that innovation-based triggering is easily incorporated into the particle filter framework, and that it vastly outperforms the classical send-on-delta scheme for certain types of nonlinear systems. We further show how the particle filter can be used to jointly precompute the future state estimates and trigger probabilities, thus... (More)
Event-based sampling has been proposed as a general technique for lowering the average communication rate, energy consumption and computational burden in remote state estimation. However, the design of the event trigger is critical for good performance. In this paper, we study the combination of innovation-based triggering and state estimation of nonlinear dynamical systems using the particle filter. It is found that innovation-based triggering is easily incorporated into the particle filter framework, and that it vastly outperforms the classical send-on-delta scheme for certain types of nonlinear systems. We further show how the particle filter can be used to jointly precompute the future state estimates and trigger probabilities, thus eliminating the need for periodic observer-to-sensor communication, at the cost of increased computational burden at the observer. For wireless, battery-powered sensors, this enables the radio to be turned off between sampling events, which is key to saving energy.
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Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
event-based estimation, state estimation, particle filter
host publication
2020 19th European Control Conference (ECC)
pages
8 pages
conference name
European Control Conference (ECC 20)
conference location
Saint Petersburg, Russian Federation
conference dates
2020-05-12 - 2020-05-15
external identifiers
  • scopus:85090159816
project
Event-Based Information Fusion for the Self-Adaptive Cloud
language
English
LU publication?
yes
id
dd59b5c4-9665-42d3-bb49-18faaff16f3a
date added to LUP
2020-02-27 09:03:10
date last changed
2022-05-12 00:45:50
@inproceedings{dd59b5c4-9665-42d3-bb49-18faaff16f3a,
  abstract     = {{Event-based sampling has been proposed as a general technique for lowering the average communication rate, energy consumption and computational burden in remote state estimation. However, the design of the event trigger is critical for good performance. In this paper, we study the combination of innovation-based triggering and state estimation of nonlinear dynamical systems using the particle filter. It is found that innovation-based triggering is easily incorporated into the particle filter framework, and that it vastly outperforms the classical send-on-delta scheme for certain types of nonlinear systems. We further show how the particle filter can be used to jointly precompute the future state estimates and trigger probabilities, thus eliminating the need for periodic observer-to-sensor communication, at the cost of increased computational burden at the observer. For wireless, battery-powered sensors, this enables the radio to be turned off between sampling events, which is key to saving energy.<br/>}},
  author       = {{Ruuskanen, Johan and Cervin, Anton}},
  booktitle    = {{2020 19th European Control Conference (ECC)}},
  keywords     = {{event-based estimation; state estimation; particle filter}},
  language     = {{eng}},
  title        = {{On Innovation-Based Triggering for Event-Based Nonlinear State Estimation Using the Particle Filter}},
  url          = {{https://lup.lub.lu.se/search/files/76639897/ECC2020.pdf}},
  year         = {{2020}},
}