On Innovation-Based Triggering for Event-Based Nonlinear State Estimation Using the Particle Filter
(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:
https://lup.lub.lu.se/record/dd59b5c4-9665-42d3-bb49-18faaff16f3a
- author
- Ruuskanen, Johan LU and Cervin, Anton LU
- organization
- publishing date
- 2020
- 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}}, }