Self-recovering extended Kalman filtering algorithm based on model-based diagnosis and resetting using an assisting FIR filter

Jung Min Pak; Choon Ki Ahn; Peng Shi; Myo Taeg Lim

doi:10.1016/j.neucom.2015.08.011

Self-recovering extended Kalman filtering algorithm based on model-based diagnosis and resetting using an assisting FIR filter

Jung Min Pak, Choon Ki Ahn, Peng Shi, Myo Taeg Lim

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

32 Citations (Scopus)

Abstract

This paper proposes a new intelligent filtering algorithm called the self-recovering extended Kalman filter (SREKF). In the SREKF algorithm, the EKF[U+05F3]s failure or abnormal operation is automatically diagnosed using an intelligence algorithm for model-based diagnosis. When the failure is diagnosed, an assisting filter, a nonlinear finite impulse response (FIR) filter, is operated. Using the output of the nonlinear FIR filter, the EKF is reset and rebooted. In this way, the SREKF can self-recover from failures. The effectiveness and performance of the proposed SREKF are demonstrated through two applications - the frequency estimation and the indoor human localization.

Original language	English
Pages (from-to)	645-658
Number of pages	14
Journal	Neurocomputing
Volume	173
DOIs	https://doi.org/10.1016/j.neucom.2015.08.011
Publication status	Published - 2016 Jan 15

Bibliographical note

Funding Information:
This work was supported partially by General Research Program through the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education ( NRF-2013R1A1A2008698 ), partially by NRF funded by the Ministry of Science, ICT & Future Planning ( NRF-2014R1A1A1006101 ), and partially by “Human Resources program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20154030200610 ).

Publisher Copyright:
© 2015 Elsevier B.V.

Keywords

Finite impulse response (FIR) filter
Frequency estimation
Indoor localization
Self-recovering extended Kalman filter (SREKF)

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2015.08.011

Cite this

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title = "Self-recovering extended Kalman filtering algorithm based on model-based diagnosis and resetting using an assisting FIR filter",

abstract = "This paper proposes a new intelligent filtering algorithm called the self-recovering extended Kalman filter (SREKF). In the SREKF algorithm, the EKF[U+05F3]s failure or abnormal operation is automatically diagnosed using an intelligence algorithm for model-based diagnosis. When the failure is diagnosed, an assisting filter, a nonlinear finite impulse response (FIR) filter, is operated. Using the output of the nonlinear FIR filter, the EKF is reset and rebooted. In this way, the SREKF can self-recover from failures. The effectiveness and performance of the proposed SREKF are demonstrated through two applications - the frequency estimation and the indoor human localization.",

keywords = "Finite impulse response (FIR) filter, Frequency estimation, Indoor localization, Self-recovering extended Kalman filter (SREKF)",

author = "Pak, {Jung Min} and Ahn, {Choon Ki} and Peng Shi and Lim, {Myo Taeg}",

note = "Funding Information: This work was supported partially by General Research Program through the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education ( NRF-2013R1A1A2008698 ), partially by NRF funded by the Ministry of Science, ICT & Future Planning ( NRF-2014R1A1A1006101 ), and partially by “Human Resources program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20154030200610 ). Publisher Copyright: {\textcopyright} 2015 Elsevier B.V.",

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doi = "10.1016/j.neucom.2015.08.011",

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AU - Pak, Jung Min

AU - Ahn, Choon Ki

AU - Shi, Peng

AU - Lim, Myo Taeg

N1 - Funding Information: This work was supported partially by General Research Program through the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education ( NRF-2013R1A1A2008698 ), partially by NRF funded by the Ministry of Science, ICT & Future Planning ( NRF-2014R1A1A1006101 ), and partially by “Human Resources program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20154030200610 ). Publisher Copyright: © 2015 Elsevier B.V.

PY - 2016/1/15

Y1 - 2016/1/15

N2 - This paper proposes a new intelligent filtering algorithm called the self-recovering extended Kalman filter (SREKF). In the SREKF algorithm, the EKF[U+05F3]s failure or abnormal operation is automatically diagnosed using an intelligence algorithm for model-based diagnosis. When the failure is diagnosed, an assisting filter, a nonlinear finite impulse response (FIR) filter, is operated. Using the output of the nonlinear FIR filter, the EKF is reset and rebooted. In this way, the SREKF can self-recover from failures. The effectiveness and performance of the proposed SREKF are demonstrated through two applications - the frequency estimation and the indoor human localization.

AB - This paper proposes a new intelligent filtering algorithm called the self-recovering extended Kalman filter (SREKF). In the SREKF algorithm, the EKF[U+05F3]s failure or abnormal operation is automatically diagnosed using an intelligence algorithm for model-based diagnosis. When the failure is diagnosed, an assisting filter, a nonlinear finite impulse response (FIR) filter, is operated. Using the output of the nonlinear FIR filter, the EKF is reset and rebooted. In this way, the SREKF can self-recover from failures. The effectiveness and performance of the proposed SREKF are demonstrated through two applications - the frequency estimation and the indoor human localization.

KW - Finite impulse response (FIR) filter

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KW - Indoor localization

KW - Self-recovering extended Kalman filter (SREKF)

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Self-recovering extended Kalman filtering algorithm based on model-based diagnosis and resetting using an assisting FIR filter

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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