Abstract
The extended Kalman Filter (EKF) is an important method for eliminating stochastic errors of dynamic position in the Global Positioning System (GPS). One of the adaptive methods is called the adaptive fading Kalman filter (AFKF), which employs suboptimal fading factors for solving the divergence problem in an EKF. Incorporation of a scaling factor has been proposed for tuning the fading factors so as to improve the tracking capability. A novel scheme called the fuzzy adaptive fading Kalman filter (FAFKF) is proposed. In the FAFKF, the fuzzy logic reasoning system is incorporated into the AFKF. By monitoring the degree of divergence (DOD) parameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the scaling factor according to the change in vehicle dynamics. GPS navigation processing using the FAFKF will be simulated to validate the effectiveness of the proposed strategy.
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Jwo, DJ., Chang, FI. (2007). A Fuzzy Adaptive Fading Kalman Filter for GPS Navigation. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_82
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DOI: https://doi.org/10.1007/978-3-540-74171-8_82
Publisher Name: Springer, Berlin, Heidelberg
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