Article Dans Une Revue IEEE Transactions on Automatic Control Année : 2017

Linear minimum mean square filters for Markov jump linear systems

Résumé

New linear minimum mean square estimators are introduced in this paper by considering a cluster information structure in the filter design. The set of filters constructed in this way can be ordered in a lattice according to the refines of clusters of the Markov chain, including the linear Markovian estimator at one end (with only one cluster) and the Kalman filter at the other hand (with as many clusters as Markov states). The higher is the number of clusters, the heavier are pre-compuations and smaller is the estimation error, so that the cluster cardinality allows for a trade-off between performance and computational burden. In this paper we propose the estimator, give the formulas for pre-computation of gains, present some properties, and give an illustrative numerical example.

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Est décrite par hal-01563236 Objet présenté à une conférence Eduardo Costa, Benoîte de Saporta. Clustered Information Filter for Markov Jump Linear Systems. CT 2017 - SIAM Conference on Control and Its Applications, Jul 2017, Pittsburgh, United States. ⟨hal-01563236⟩

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hal-01251334 , version 1 (06-01-2016)

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Eduardo Costa, Benoîte de Saporta. Linear minimum mean square filters for Markov jump linear systems. IEEE Transactions on Automatic Control, 2017, 62 (7), pp.3567-3572. ⟨10.1109/TAC.2017.2692180⟩. ⟨hal-01251334⟩
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