Abstract
This paper proposes a new intelligent filtering algorithm called the self-recovering extended Kalman filter (SREKF). In the SREKF algorithm, the EKF'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 |
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Journal | Neurocomputing |
DOIs | |
Publication status | Accepted/In press - 2015 Apr 17 |
Keywords
- Finite impulse response (FIR) filter
- Frequency estimation
- Indoor localization
- Self-recovering extended Kalman filter (SREKF)
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Cognitive Neuroscience