An Innovative Fuzzy Adaptive Fading Kalman Filter for GPS Navigation

• Main Authors: Fu-I Chang, 張復詒
• Other Authors: Dah-Jing Jwo
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/76754239283236996074

碩士 === 國立臺灣海洋大學 === 通訊與導航工程系 === 95 === 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 multiple fading factors for limiting the length of memory in an EKF. A scaling factor α has been proposed for increasing the fading factors so as to improve the tracking capability. Traditional approach for selecting the scaling factor α heavily relies on personal experience or computer simulation. In order to resolve this shortcoming, a novel scheme called the fuzzy adaptive fading Kalman filter (FAFKF) is carried out. In the FAFKF, the fuzzy logic reasoning system is incorporated into the adaptive fading Kalman filter. 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. The performance of the proposed scheme will be assessed and compared to those of conventional EKF and AFKF.