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

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

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.

Details

Authors
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Control Engineering

Keywords

  • event-based estimation, state estimation, particle filter
Original languageEnglish
Title of host publication2020 19th European Control Conference (ECC)
Number of pages8
Publication statusPublished - 2020
Publication categoryResearch
Peer-reviewedYes
EventEuropean Control Conference (ECC 20) - Saint Petersburg, Russian Federation
Duration: 2020 May 122020 May 15
https://ecc20.eu/

Conference

ConferenceEuropean Control Conference (ECC 20)
CountryRussian Federation
CitySaint Petersburg
Period2020/05/122020/05/15
Internet address

Total downloads

No data available

Related projects

View all (1)