An improvement to the interacting multiple model (IMM) algorithm

TitleAn improvement to the interacting multiple model (IMM) algorithm
Publication TypeJournal Article
Year of Publication2001
AuthorsJohnston, L. A., and V. Krishnamurthy
JournalSignal Processing, IEEE Transactions on
Pagination2909 -2923
Date Publisheddec.
Keywordsalternating expectation conditional maximization, Bayes methods, computer simulations, conditional mean state estimator, EM algorithm, exponential complexity, filtering theory, generalized pseudo-Bayesian scheme, IMM algorithm, interacting multiple model algorithm, jump Markov linear system, Markov processes, matched filters, mode-matched filtering, optimal conditional mean state estimate, optimisation, recursive estimation, recursive MAP state sequence estimator, reweighted IMM algorithm, reweighted interacting multiple model algorithm, sequential estimation, state estimation, suboptimal multiple model filtering algorithms, target tracking

Computing the optimal conditional mean state estimate for a jump Markov linear system requires exponential complexity, and hence, practical filtering algorithms are necessarily suboptimal. In the target tracking literature, suboptimal multiple-model filtering algorithms, such as the interacting multiple model (IMM) method and generalized pseudo-Bayesian (GPB) schemes, are widely used for state estimation of such systems. We derive a reweighted interacting multiple model algorithm. Although the IMM algorithm is an approximation of the conditional mean state estimator, our algorithm is a recursive implementation of a maximum a posteriori (MAP) state sequence estimator. This MAP estimator is an instance of a previous version of the EM algorithm known as the alternating expectation conditional maximization (AECM) algorithm. Computer simulations indicate that the proposed reweighted IMM algorithm is a competitive alternative to the popular IMM algorithm and GPB methods


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