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

自动化科学与电气工程学院

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57 引用（Scopus）

摘要

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.

源语言	英语
文章编号	7272832
页（从-至）	1811-1825
页数	15
期刊	IEEE Transactions on Aerospace and Electronic Systems
卷	51
期	3
DOI	https://doi.org/10.1109/TAES.2015.140542
出版状态	已出版 - 1 7月 2015

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10.1109/TAES.2015.140542

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An information theoretic approach to interacting multiple model estimation

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