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Optimal energy allocation for Kalman filtering over packet dropping links with imperfect acknowledgements and energy harvesting constraints
Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, VIC 3052, Australien.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
2014 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 59, no 8, 2128-2143 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents a design methodology for optimal transmission energy allocation at a sensor equipped with energy harvesting technology for remote state estimation of linear stochastic dynamical systems. In this framework, the sensor measurements as noisy versions of the system states are sent to the receiver over a packet dropping communication channel. The packet dropout probabilities of the channel depend on both the sensor's transmission energies and time varying wireless fading channel gains. The sensor has access to an energy harvesting source which is an everlasting but unreliable energy source compared to conventional batteries with fixed energy storages. The receiver performs optimal state estimation with random packet dropouts to minimize the estimation error covariances based on received measurements. The receiver also sends packet receipt acknowledgments to the sensor via an erroneous feedback communication channel which is itself packet dropping. The objective is to design optimal transmission energy allocation at the energy harvesting sensor to minimize either a finite-time horizon sum or a long term average (infinite-time horizon) of the trace of the expected estimation error covariance of the receiver's Kalman filter. These problems are formulated as Markov decision processes with imperfect state information. The optimal transmission energy allocation policies are obtained by the use of dynamic programming techniques. Using the concept of submodularity, the structure of the optimal transmission energy policies are studied. Suboptimal solutions are also discussed which are far less computationally intensive than optimal solutions. Numerical simulation results are presented illustrating the performance of the energy allocation algorithms.

Place, publisher, year, edition, pages
2014. Vol. 59, no 8, 2128-2143 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering with specialization in Signal Processing
URN: urn:nbn:se:uu:diva-234999DOI: 10.1109/TAC.2014.2319011ISI: 000342923700010OAI: oai:DiVA.org:uu-234999DiVA: diva2:758694
Available from: 2014-10-28 Created: 2014-10-28 Last updated: 2014-12-05Bibliographically approved

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Dey, Subhrakanti
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