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    Remote estimation subject to packet loss and quantization noise


    Dey, Subhrakanti and Chiuso, Alessandro and Schenato, Luca (2014) Remote estimation subject to packet loss and quantization noise. In: 52nd IEEE Conference on Decision and Control. IEEE, pp. 6097-6104. ISBN 9781467357142

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    Abstract

    In this paper we consider the problem of designing coding and decoding schemes to estimate the state of a scalar stable stochastic linear system in the presence of a wireless communication channel between the sensor and the estimator. In particular, we consider a communication channel which is prone to packet loss and includes quantization noise due to its limited capacity. We study two scenarios: the first with channel feedback and the second with no channel feedback. More specifically, in the first scenario the transmitter is aware of the quantization noise and the packet loss history of the channel, while in the second scenario the transmitter is aware of the quantization noise only. We show that in the first scenario, the optimal strategy among all possible linear encoders corresponds to the transmission of the Kalman filter innovation similarly to the differential pulse-code modulation (DPCM). In the second scenario, we show that there is a critical packet loss probability above which it is better to transmit the state rather than the innovation. We also propose a heuristic strategy based on the transmission of a convex combination of the state and the Kalman filter innovation which is shown to provide a performance close to the one obtained with channel feedback.

    Item Type: Book Section
    Additional Information: Cite as: S. Dey, A. Chiuso and L. Schenato, "Remote estimation subject to packet loss and quantization noise," 52nd IEEE Conference on Decision and Control, 2013, pp. 6097-6104, doi: 10.1109/CDC.2013.6760853.
    Keywords: Remote estimation; subject; packet loss; quantization; noise;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 14500
    Identification Number: https://doi.org/10.1109/CDC.2013.6760853
    Depositing User: Subhrakanti Dey
    Date Deposited: 03 Jun 2021 14:21
    Publisher: IEEE
    Refereed: Yes
    URI:

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