Proceedings ArticleDOI
On the choice of the event trigger in event-based estimation
Sebastian Trimpe,Marco C. Campi +1 more
- pp 1-8
TLDR
Analysis of the information pattern underlying the triggering decision reveals a fundamental advantage of triggers employing the real-time measurement in their decision over those that do not (VBT), and numerical simulation studies support this finding and provide a quantitative evaluation of the triggers in terms of their average estimation versus communication performance.Abstract:
In event-based state estimation, the event trigger decides whether or not a measurement is used for updating the state estimate. In a remote estimation scenario, this allows for trading off estimation performance for communication, and thus saving resources. In this paper, popular event triggers for estimation, such as send-on-delta (SoD), measurement-based triggering (MBT), variance-based triggering (VBT), and relevant sampling (RS), are compared for the scenario of a scalar linear process with Gaussian noise. First, the analysis of the information pattern underlying the triggering decision reveals a fundamental advantage of triggers employing the real-time measurement in their decision (such as MBT, RS) over those that do not (VBT). Second, numerical simulation studies support this finding and, moreover, provide a quantitative evaluation of the triggers in terms of their average estimation versus communication performance.read more
Citations
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Journal ArticleDOI
Event-Based Estimation With Information-Based Triggering and Adaptive Update
TL;DR: An open-loop estimation strategy with an information-based triggering mechanism coupled with an adaptive event-based fusion framework and an unsupervised fusion model at the estimation side that significantly outperforms its counterparts specifically in low communication rates.
Journal ArticleDOI
An information aware event-triggered scheme for particle filter based remote state estimation
TL;DR: A novel event-based transmission scheme is proposed where the Kullback–Leibler divergence is used to identify informative measurements, thereby enabling each sensor to quantify the informativeness of its current measurement without running a copy of the estimator.
Journal ArticleDOI
Distributed Event-Based State Estimation for Networked Systems: An LMI Approach
TL;DR: A synthesis procedure is proposed for designing the agents’ state estimators and the event triggering thresholds and the resulting distributed and event-based control system is guaranteed to be stable and to satisfy a predefined estimation performance criterion.
Proceedings ArticleDOI
Event-Triggered Learning for Resource-Efficient Networked Control
TL;DR: In this article, the authors proposed event-triggered learning (ETL) as a novel concept to further reduce communication, where whenever poor communication performance is detected, an identification experiment is triggered and an improved prediction model is learned from data.
Journal ArticleDOI
Distributed Kalman consensus filter with event-triggered communication: Formulation and stability analysis
Cui Zhang,Yingmin Jia +1 more
TL;DR: The suboptimal event-triggered Kalman consensus filter is proposed in order to reduce the computational complexity in covariance propagation and the formal stability analysis of the estimation error is provided by using the Lyapunov-based approach.
References
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Proceedings ArticleDOI
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