An adaptive fuzzy strong tracking Kalman filter for GPS/INS navigation

The Kalman filtering is a form of recursive optimal estimation, which has been widely applied to the navigation designs. Kalman filter requires that all the plant dynamics and noise processes are exactly known, and the noise process is zero mean white noise. If the theoretical behavior of a filter and its actual behavior do not agree, divergence problems will occur. The adaptive algorithm has been one of the approaches to prevent divergence problem of the Kalman filter when precise knowledge on the system models are not available. One of the adaptive methods called the strong tracking Kalman filter (STKF) properly employs a scaling factor, by monitoring the innovation information. Traditional approach for selecting the parameter in the STKF heavily relies on personal experience or computer simulation. In order to resolve this shortcoming. a novel scheme called the adaptive fuzzy strong tracking Kalman filter (AFSTKF) is proposed. In the AFSTKF, the fuzzy logic reasoning approach is incorporated into the STKF. By monitoring parameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the parameter according to the change in vehicle dynamics. Integrated navigation processing using the AFSTKF will be conducted to validate the effectiveness of the proposed strategy. The performance of the proposed AFSTKF scheme will be assessed and compared to those of conventional EKF and STKF.