Team-optimal distributed MMSE estimation in general and tree networks

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dc.citation.epage95en_US
dc.citation.spage83en_US
dc.citation.volumeNumber64en_US
dc.contributor.authorSayin, M. O.en_US
dc.contributor.authorKozat, S. S.en_US
dc.contributor.authorBaşar, T.en_US
dc.date.accessioned2018-04-12T11:12:34Z
dc.date.available2018-04-12T11:12:34Z
dc.date.issued2017en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe construct team-optimal estimation algorithms over distributed networks for state estimation in the finite-horizon mean-square error (MSE) sense. Here, we have a distributed collection of agents with processing and cooperation capabilities. These agents observe noisy samples of a desired state through a linear model and seek to learn this state by interacting with each other. Although this problem has attracted significant attention and been studied extensively in fields including machine learning and signal processing, all the well-known strategies do not achieve team-optimal learning performance in the finite-horizon MSE sense. To this end, we formulate the finite-horizon distributed minimum MSE (MMSE) when there is no restriction on the size of the disclosed information, i.e., oracle performance, over an arbitrary network topology. Subsequently, we show that exchange of local estimates is sufficient to achieve the oracle performance only over certain network topologies. By inspecting these network structures, we propose recursive algorithms achieving the oracle performance through the disclosure of local estimates. For practical implementations we also provide approaches to reduce the complexity of the algorithms through the time-windowing of the observations. Finally, in the numerical examples, we demonstrate the superior performance of the introduced algorithms in the finite-horizon MSE sense due to optimal estimation.en_US
dc.embargo.release2019-05-01en_US
dc.identifier.doi10.1016/j.dsp.2017.02.007en_US
dc.identifier.issn1051-2004
dc.identifier.urihttp://hdl.handle.net/11693/37405
dc.language.isoEnglishen_US
dc.publisherElsevier Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.dsp.2017.02.007en_US
dc.source.titleDigital Signal Processingen_US
dc.subjectDistributed kalman filteren_US
dc.subjectDistributed MMSE estimationen_US
dc.subjectDistributed networksen_US
dc.subjectFinite-horizonen_US
dc.subjectTeam problemen_US
dc.titleTeam-optimal distributed MMSE estimation in general and tree networksen_US
dc.typeArticleen_US

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