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 | |
Publication status | Published - 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