An information theoretic approach to interacting multiple model estimation

Wenling Li; Yingmin Jia

doi:10.1109/TAES.2015.140542

An information theoretic approach to interacting multiple model estimation

School of Automation Science and Electrical Engineering

Research output: Contribution to journal › Article › peer-review

57 Scopus citations

Abstract

We present an information theoretic approach to develop an interacting multiple model (IMM) estimator. In the mixing and output steps of the proposed estimator, the weighted Kullback-Leibler (KL) divergence is used to derive the fusion of conditional probability density functions. A lower bound and an upper bound are derived for the error covariance of controllable and observable Markov jump linear systems. Simulation results are provided to verify the effectiveness of the proposed estimator.

Original language	English
Article number	7272832
Pages (from-to)	1811-1825
Number of pages	15
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	51
Issue number	3
DOIs	https://doi.org/10.1109/TAES.2015.140542
State	Published - 1 Jul 2015

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10.1109/TAES.2015.140542

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title = "An information theoretic approach to interacting multiple model estimation",

abstract = "We present an information theoretic approach to develop an interacting multiple model (IMM) estimator. In the mixing and output steps of the proposed estimator, the weighted Kullback-Leibler (KL) divergence is used to derive the fusion of conditional probability density functions. A lower bound and an upper bound are derived for the error covariance of controllable and observable Markov jump linear systems. Simulation results are provided to verify the effectiveness of the proposed estimator.",

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AB - We present an information theoretic approach to develop an interacting multiple model (IMM) estimator. In the mixing and output steps of the proposed estimator, the weighted Kullback-Leibler (KL) divergence is used to derive the fusion of conditional probability density functions. A lower bound and an upper bound are derived for the error covariance of controllable and observable Markov jump linear systems. Simulation results are provided to verify the effectiveness of the proposed estimator.

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An information theoretic approach to interacting multiple model estimation

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