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 journalArticlepeer-review

28 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 languageEnglish
Pages (from-to)645-658
Number of pages14
JournalNeurocomputing
Volume173
DOIs
Publication statusPublished - 2016 Jan 15

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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