The Interacting Multiple Model Algorithm For Systems With Markovian Switching Coefficients

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Authors: H A P BlomYaakov Barshalom
Published in: IEEE Transactions On Automatic Control
Year: 1988   
DOI: 10.1109/9.1299
Citations: 920
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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