Eva Julia Schmitt, Benjamin Noack, Wolfgang Krippner, Uwe D. Hanebeck
Gaussianity-Preserving Event-Based State Estimation with an FIR-Based Stochastic Trigger IEEE Control Systems Letters, vol. 3, no. 3, pp. 769–774, July, 2019.
With modern communication technology, sensors, estimators, and controllers can be pushed apart to distribute
intelligence over wide distances. Instead of congesting channels by periodic data transmissions, smart sensors can decide
on their own whether data are worth transmitting. This paper studies event-based transmissions from sensor to estimator.
The sensor-side event trigger conveys usable information even if no transmission is triggered. In the absence of data,
such implicit information can still be exploited by the remote Kalman filter. For this purpose, an easy-to-implement
triggering mechanism is proposed based on a Finite Impulse Response prediction that is compared against a stochastic
decision variable. By the aid of the stochastic event trigger, the implicit information retains a Gaussian representation
and can easily be processed by the Kalman filter. The parameters for the stochastic trigger are retrieved from the
Finite Impulse Response filter, which contributes to reducing the communication rate significantly, as shown in simulations.
@article{LCSS19_Schmitt,
title = {{Gaussianity-Preserving Event-Based State Estimation with an FIR-Based Stochastic Trigger}},
author = {Eva Julia Schmitt and Benjamin Noack and Wolfgang Krippner and Uwe D. Hanebeck},
doi = {10.1109/LCSYS.2019.2918024},
issn = {2475-1456},
journal = {IEEE Control Systems Letters},
month = jul,
number = {3},
pages = {769--774},
volume = {3},
year = {2019}
}
Conference Proceedings
Eva Julia Schmitt, Irene Perez-Salesa, Benjamin Noack, Carlos Sagues
NN-based and Handcrafted Stochastic Dynamic Event-Triggering Mechanisms for Event-based Estimation (accepted) Proceedings of the 23rd IFAC World Congress (IFAC 2026), Busan, South Korea, August, 2026.
BibTeX
@inproceedings{IFAC26_Schmitt,
title = {{NN-based and Handcrafted Stochastic Dynamic Event-Triggering Mechanisms for Event-based Estimation (accepted)}},
author = {Eva Julia Schmitt and Irene Perez-Salesa and Benjamin Noack and Carlos Sagues},
booktitle = {Proceedings of the 23rd IFAC World Congress (IFAC 2026)},
address = {Busan, South Korea},
month = aug,
year = {2026}
}
Eva Julia Schmitt, Benjamin Noack
Latency-aware Event-based State Estimation Using a Sampling-based Approach (accepted) Proceedings of the 29th International Conference on Information Fusion (FUSION 2026), Trondheim, Norway, June, 2026.
BibTeX
@inproceedings{Fusion26_Schmitt,
title = {{Latency-aware Event-based State Estimation Using a Sampling-based Approach (accepted)}},
author = {Eva Julia Schmitt and Benjamin Noack},
booktitle = {Proceedings of the 29th International Conference on Information Fusion (FUSION 2026)},
address = {Trondheim, Norway},
month = jun,
year = {2026}
}
Eva Julia Schmitt, Benjamin Noack
On Consistency-preserving Stochastic Event Trigger Design Proceedings of the 64th IEEE Conference on Decision and Control (CDC 2025), Rio de Janeiro, Brazil, December, 2025.
Event-based transmissions and estimation can effectively reduce the burden on the communications system in spatially distributed sensing and estimation setups while maintaining good estimation performance. This can be achieved by transmitting only those sensor measurements that fulfill a predefined transmission policy and using a remote estimator that can exploit knowledge of the triggering condition in non-transmission instants. However, it is crucial to ensure the consistency of the remote estimator to obtain reliable estimates. Depending on the transmission policy, this is difficult to achieve. In this paper, a generalized framework for stochastic event triggers is provided and design criteria for triggering policies are deduced that allow for simple estimator design. The results obtained are evaluated with the help of system simulations using a novel triggering policy developed under the proposed design criteria and show its advantages.
@inproceedings{CDC25_Schmitt,
title = {{On Consistency-preserving Stochastic Event Trigger Design}},
author = {Eva Julia Schmitt and Benjamin Noack},
booktitle = {Proceedings of the 64th IEEE Conference on Decision and Control (CDC 2025)},
address = {Rio de Janeiro, Brazil},
doi = {10.1109/CDC57313.2025.11312614},
month = dec,
year = {2025}
}
Eva Julia Schmitt, Benjamin Noack
A Unified Framework for Innovation-based Stochastic and Deterministic Event Triggers Proceedings of the 28th International Conference on Information Fusion (FUSION 2025), Rio de Janeiro, Brazil, July, 2025.
Resources such as bandwidth and energy are limited
in many wireless communications use cases, especially when
large numbers of sensors and fusion centers need to exchange
information frequently. One opportunity to overcome resource
constraints is the use of event-based transmissions and estimation
to transmit only information that contributes significantly to
the reconstruction of the system’s state. The design of efficient
triggering policies and estimators is crucial for successful event-
based transmissions. While previously deterministic and stochas-
tic event triggering policies have been treated separately, this
paper unifies the two approaches and gives insights into the
design of consistent trigger-matching estimators. Two different
estimators are presented, and different pairs of triggers and
estimators are evaluated through simulation studies.
@inproceedings{Fusion25_Schmitt,
title = {{A Unified Framework for Innovation-based Stochastic and Deterministic Event Triggers}},
author = {Eva Julia Schmitt and Benjamin Noack},
booktitle = {Proceedings of the 28th International Conference on Information Fusion (FUSION 2025)},
address = {Rio de Janeiro, Brazil},
doi = {10.23919/FUSION65864.2025.11124042},
month = jul,
year = {2025}
}
Eva Julia Schmitt, Benjamin Noack
Consistent Stochastic Event-based Estimation Under Packet Losses Using Low-Cost Sensors Proceedings of the 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2024), Pilsen, Czech Republic, September, 2024.
Reliably monitoring the environment with distributed sensors is a necessity for many modern automation tasks such as automated driving. However, the growing demand for communications resources can hardly be fulfilled in the future without a paradigm shift in resource utilization. One way to leverage the burden on the communications system is to transmit data in an event-based fashion rather than periodically at a high rate. Several event-based triggers and estimators have been proposed in the past. Unfortunately, the event-based schemes are often sensitive to imperfections in the communications system such as packet losses. To ensure reliable estimates under packet losses, a new stochastic event-based scheme is proposed that uses the transmission probability of the trigger as an additional periodic information source in the estimator on the receiver side. The effectiveness of the approach is evaluated in simulation using different packet loss models.
@inproceedings{MFI24_Schmitt,
title = {{Consistent Stochastic Event-based Estimation Under Packet Losses Using Low-Cost Sensors}},
author = {Eva Julia Schmitt and Benjamin Noack},
booktitle = {Proceedings of the 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2024)},
address = {Pilsen, Czech Republic},
doi = {10.1109/MFI62651.2024.10705785},
month = sep,
year = {2024}
}
Eva Julia Schmitt, Benjamin Noack
Event-based Multisensor Fusion with Correlated Estimates Proceedings of the 27th International Conference on Information Fusion (FUSION 2024), Venice, Italy, July, 2024.
Many automation tasks require to fuse information that is acquired by distributed sensors and passed through a wireless network across multiple nodes. The growing number of connected sensors and agents increases the burden on the communications network and the energy consumption. Further challenges in information fusion arise from correlated data shared between nodes. To mitigate the negative effects, an efficient multi-sensor fusion approach is presented in this paper. A system design that uses stochastic event-based instead of periodic transmissions is proposed based on two different algorithms, the augmented state approach and fast covariance intersection. Furthermore, two different network topologies are investigated and a methodology to handle correlations among both finite impulse response and recursive estimates is developed. Together, the results represent a wide range of network topologies and possible correlation structures and give insights into the estimation performance and network utilization.
@inproceedings{Fusion24_Schmitt,
title = {{Event-based Multisensor Fusion with Correlated Estimates}},
author = {Eva Julia Schmitt and Benjamin Noack},
booktitle = {Proceedings of the 27th International Conference on Information Fusion (FUSION 2024)},
address = {Venice, Italy},
doi = {10.23919/FUSION59988.2024.10706368},
month = jul,
year = {2024}
}
Eva Julia Schmitt, Benjamin Noack
Event-based Colored-Noise Kalman Filtering for Improved Resource Efficiency Proceedings of the combined IEEE 2023 Symposium Sensor Data
Fusion and International Conference on Multisensor Fusion and
Integration (SDF-MFI 2023), Bonn, Germany, November, 2023.
In modern automated systems, the number of agents and sensors is rapidly increasing and with them the energy consumption and the burden on communications networks. One way to increase the energy and spectral efficiency in sensor networks is to replace periodic transmissions between nodes with event-based transmissions and to use matching estimation techniques at the receiving nodes. Since multi-sensor systems and smart event-trigger designs often lead to correlations between measurements due to correlated process and measurement noise, estimators that can handle such correlations are required to guarantee good performance. In this paper, an event-based colored-noise Kalman filter was developed and specifically designed for a finite impulse response-based stochastic trigger. Nevertheless, the concept is suitable for a wide class of correlated input data.
@inproceedings{MFI23_Schmitt,
title = {{Event-based Colored-Noise Kalman Filtering for Improved Resource Efficiency}},
author = {Eva Julia Schmitt and Benjamin Noack},
booktitle = {Proceedings of the combined IEEE 2023 Symposium Sensor Data
Fusion and International Conference on Multisensor Fusion and
Integration (SDF-MFI 2023)},
address = {Bonn, Germany},
doi = {10.1109/SDF-MFI59545.2023.10361406},
month = nov,
year = {2023}
}
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