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An Outlier-Robust Kalman Filter With Adaptive Selection of Elliptically Contoured Distributions

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posted on 2022-03-14, 16:33 authored by C Xue, Y Huang, F Zhu, Y Zhang, J Chambers
In this paper, elliptically contoured (EC) distributions are used to model outlier-contaminated measurement noises. Exploiting a heuristic approach to introduce an unknown parameter, we present an analytical update form of the joint posterior probability density function of the state vector and auxiliary random variable, from which a novel robust EC distributions-based Kalman filtering framework is first derived. To illustrate the effectiveness of the proposed framework, the convergence, robustness, optimality and computational complexity analyses of the proposed method are then given. In addition, to cope with complex noise environments, the interaction multiple model is employed to achieve the adaptive selection of EC distributions such that well-behaved estimation performance can be obtained for different noise cases. Simulation results demonstrate the validity and superiority of the proposed algorithm.

Funding

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61903097 and 62173105)

10.13039/501100012226-Fundamental Research Funds for the Central Universities (Grant Number: 3072021CFT0401)

History

Citation

IEEE Transactions on Signal Processing ( Volume: 70), 99. 994-1009

Author affiliation

School of Engineering

Version

  • AM (Accepted Manuscript)

Published in

IEEE Transactions on Signal Processing

Volume

70

Pagination

994-1009

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

1053-587X

eissn

1941-0476

Copyright date

2022

Available date

2022-03-14

Language

en

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