The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Stochastic Event-Triggered Sensor Schedule for Remote State Estimation
Open accessAbstract
We propose an open-loop and a closed-loop stochastic event-triggered sensor schedule for remote state estimation. Both schedules overcome the essential difficulties of existing schedules in recent literature works where, through introducing a deterministic event-triggering mechanism, the Gaussian property of the innovation process is destroyed which produces a challenging nonlinear filtering problem that cannot be solved unless approximation techniques are adopted. The proposed stochastic event-triggered sensor schedules eliminate such approximations. Under these two schedules, the minimum mean squared error (MMSE) estimator and its estimation error covariance matrix at the remote estimator are given in a closed-form. The stability in terms of the expected error covariance and the sample path of the error covariance for both schedules is studied. We also formulate and solve an optimization problem to obtain the minimum communication rate under some estimation quality constraint using the open-loop sensor schedule. A numerical comparison between the closed-loop MMSE estimator and a typical approximate MMSE estimator with deterministic event-triggered sensor schedule, in a problem setting of target tracking, shows the superiority of the proposed sensor schedule.
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Cites
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Kalman Filtering With Intermittent Observations
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Self-citationEstimating Optimal Tracking Filter Performance for Manned Maneuvering Targets
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Event-Based Sensor Data Scheduling: Trade-Off Between Communication Rate and Estimation Quality
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Self-citationUniversal Decentralized Estimation in a Bandwidth Constrained Sensor Network
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Cited by
Zhang, X.-M., Han, Q.-L., & Zhang, B.-L. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2607150 · Cited by 772
Event-triggered communication and control of networked systems for multi-agent consensus
Nowzari, C., Garcia, E., & Cortés, J. (2019) · Automatica · doi:10.1016/j.automatica.2019.03.009 · Cited by 655
Distributed Online Optimization in Dynamic Environments Using Mirror Descent
Shahrampour, S., & Jadbabaie, A. (2018) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2017.2743462 · Cited by 331
Metadata
- Authors
- Han, D., Mo, Y., Wu, J., Weerakkody, S., Sinopoli, B., & Shi, L.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2015-10
- Volume
- 60
- Issue
- 10
- Pages
- 2661-2675
- ISSN
- 0018-9286 (ISSN-L)
- 1558-2523
- OpenAlex
- W2165519686
- Cited by
- 393
- References
- 17
- FWCI
- 36.19
- Type
- journal article
- Language
- en
- Open access
- green
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