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A distributed pareto-optimal dynamic estimation method
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9810-3478
2015 (English)In: 2015 European Control Conference, ECC 2015, Institute of Electrical and Electronics Engineers (IEEE), 2015, 3673-3680 p.Conference paper (Refereed)Text
Abstract [en]

In this paper, a novel distributed model-based prediction method is proposed using sensor networks. Each sensor communicates with the neighboring nodes for state estimation based on a consensus protocol without centralized coordination. The proposed distributed estimator consists of a consensus-filtering scheme, which uses a weighted combination of sensors information, and a model-based predictor. Both the consensus-filtering weights and the model-based prediction parameter for all the state components are jointly optimized to minimize the variance and bias of the prediction error in a Pareto framework. It is assumed that the weights of the consensus-filtering phase are unequal for the different state components, unlike consensus-based approaches from literature. The state, the measurements, and the noise components are assumed to be individually correlated, but no probability distribution knowledge is assumed for the noise variables. The optimal weights are derived and it is established that the consensus-filtering weights and the model-based prediction parameters cannot be designed separately in an optimal way. The asymptotic convergence of the mean of the prediction error is demonstrated. Simulation results show the performance of the proposed method, obtaining better results than distributed Kalman filtering. © 2015 EUCA.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. 3673-3680 p.
Keyword [en]
Estimation, Forecasting, Pareto principle, Probability distributions, Sensor networks, Asymptotic convergence, Consensus protocols, Distributed modeling, Filtering schemes, Model-based prediction, Neighboring nodes, Noise components, Prediction errors, Information filtering
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-186824DOI: 10.1109/ECC.2015.7331101ScopusID: 2-s2.0-84963804551ISBN: 9783952426937OAI: oai:DiVA.org:kth-186824DiVA: diva2:928526
Conference
European Control Conference, ECC 2015, 15 July 2015 through 17 July 2015
Note

QC 20160516

Available from: 2016-05-16 Created: 2016-05-13 Last updated: 2016-05-16Bibliographically approved

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ReferencesLink to record
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