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Optimal Parameter Estimation Under Controlled Communication Over Sensor Networks.

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Title: Optimal Parameter Estimation Under Controlled Communication Over Sensor Networks.
Authors: Han, Duo1, You, Keyou2, Xie, Lihua3, Wu, Junfeng4, Shi, Ling1
Source: IEEE Transactions on Signal Processing. Dec2015, Vol. 63 Issue 24, p6473-6485. 13p.
Subjects: Linear systems, Mathematical models, Sensor networks, Wireless sensor networks, Maximum likelihood statistics, Stochastic analysis
Abstract: This paper considers parameter estimation of linear systems under sensor-to-estimator communication constraint. Due to the limited battery power and the traffic congestion over a large sensor network, each sensor is required to reduce the rate of communication between the estimator and itself. We propose an observation-driven sensor scheduling policy such that the sensor transmits only the important measurements to the estimator. Unlike the existing deterministic scheduler, our stochastic scheduling is smartly designed to well compensate for the loss of the Gaussianity of the system. This results in a nice feature that the maximum-likelihood estimator (MLE) is still able to be recursively computed in a closed form, and the resulting estimation performance can be explicitly evaluated. Moreover, an optimization problem is formulated and solved to obtain the best parameters of the scheduling policy under which the estimation performance becomes comparable to the standard MLE with full measurements under a moderate transmission rate. Finally, simulations are included to validate the theoretical results. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:This paper considers parameter estimation of linear systems under sensor-to-estimator communication constraint. Due to the limited battery power and the traffic congestion over a large sensor network, each sensor is required to reduce the rate of communication between the estimator and itself. We propose an observation-driven sensor scheduling policy such that the sensor transmits only the important measurements to the estimator. Unlike the existing deterministic scheduler, our stochastic scheduling is smartly designed to well compensate for the loss of the Gaussianity of the system. This results in a nice feature that the maximum-likelihood estimator (MLE) is still able to be recursively computed in a closed form, and the resulting estimation performance can be explicitly evaluated. Moreover, an optimization problem is formulated and solved to obtain the best parameters of the scheduling policy under which the estimation performance becomes comparable to the standard MLE with full measurements under a moderate transmission rate. Finally, simulations are included to validate the theoretical results. [ABSTRACT FROM AUTHOR]
ISSN:1053587X
DOI:10.1109/TSP.2015.2469639