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    Sensor Scheduling in Variance Based Event Triggered Estimation With Packet Drops


    Leong, Alex S. and Dey, Subhrakanti and Quevedo, Daniel E. (2017) Sensor Scheduling in Variance Based Event Triggered Estimation With Packet Drops. IEEE Transactions on Automatic Control, 62 (4). pp. 1880-1895. ISSN 0018-9286

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    Abstract

    This paper considers a remote state estimation problem with multiple sensors observing a dynamical process, where sensors transmit local state estimates over an independent and identically distributed (i.i.d.) packet dropping channel to a remote estimator. At every discrete time instant, the remote estimator decides whether each sensor should transmit or not, with each sensor transmission incurring a fixed energy cost. The channel is shared such that collisions will occur if more than one sensor transmits at a time. Performance is quantified via an optimization problem that minimizes a convex combination of the expected estimation error covariance at the remote estimator and expected energy usage across the sensors. For transmission schedules dependent only on the estimation error covariance at the remote estimator, this work establishes structural results on the optimal scheduling which show that: 1) for unstable systems, if the error covariance is large then a sensor will always be scheduled to transmit and 2) there is a threshold-type behavior in switching from one sensor transmitting to another. Specializing to the single sensor case, these structural results demonstrate that a threshold policy (i.e., transmit if the error covariance exceeds a certain threshold and don't transmit otherwise) is optimal. We also consider the situation where sensors transmit measurements instead of state estimates, and establish structural results including the optimality of threshold policies for the single sensor, scalar case. These results provide a theoretical justification for the use of such threshold policies in variance based event triggered estimation. Numerical studies confirm the qualitative behavior predicted by our structural results.

    Item Type: Article
    Keywords: Event triggered estimation; Kalman filtering; packet drops; sensor networks; sensor scheduling;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 11464
    Identification Number: https://doi.org/10.1109/TAC.2016.2602499
    Depositing User: Subhrakanti Dey
    Date Deposited: 24 Oct 2019 14:11
    Journal or Publication Title: IEEE Transactions on Automatic Control
    Publisher: IEEE
    Refereed: Yes
    URI:

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