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.
Bibliographical noteFunding 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 ).
© 2015 Elsevier B.V.
- 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