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The interacting multiple model algorithm for systems with Markovian switching coefficients

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2 Author(s)
Blom, H.A.P. ; Nat. Aerosp. Lab., Amsterdam, Netherlands ; Bar-Shalom, Y.

An important problem in filtering for linear systems with Markovian switching coefficients (dynamic multiple model systems) is the management of hypotheses, which is necessary to limit the computational requirements. A novel approach to hypotheses merging is presented for this problem. The novelty lies in the timing of hypotheses merging. When applied to the problem of filtering for a linear system with Markovian coefficients, the method is an elegant way to derive the interacting-multiple-model (IMM) algorithm. Evaluation of the IMM algorithm shows that it performs well at a relatively low computational load. These results imply a significant change in the state of the art of approximate Bayesian filtering for systems with Markovian coefficients

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Automatic Control, IEEE Transactions on  (Volume:33 ,  Issue: 8 )