2014
DOI: 10.1109/tcns.2014.2337961
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Remote Estimation With Noisy Measurements Subject to Packet Loss and Quantization Noise

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 subject to noisy measurements and 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 s… Show more

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Cited by 33 publications
(34 citation statements)
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“…In this work we extend the results of [19] and [20] by considering the possibility to pre-process the raw measurement at the transmitter. We show that the optimal strategy when full channel feedback is available at the transmitter is to send the difference from the estimated state at the transmitter and the predicted state at the receiver as in [19] and to build a Kalman filter and a state feedback with constant gain at the receiver as in [20].…”
Section: Introductionmentioning
confidence: 93%
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“…In this work we extend the results of [19] and [20] by considering the possibility to pre-process the raw measurement at the transmitter. We show that the optimal strategy when full channel feedback is available at the transmitter is to send the difference from the estimated state at the transmitter and the predicted state at the receiver as in [19] and to build a Kalman filter and a state feedback with constant gain at the receiver as in [20].…”
Section: Introductionmentioning
confidence: 93%
“…We show that the optimal strategy when full channel feedback is available at the transmitter is to send the difference from the estimated state at the transmitter and the predicted state at the receiver as in [19] and to build a Kalman filter and a state feedback with constant gain at the receiver as in [20]. However, although the performance is improved as compared to strategy proposed in [20], the stability region is the same.…”
Section: Introductionmentioning
confidence: 95%
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