Kalman filter requires that the process noises to be zero mean white noise; otherwise, the divergence will occur. Adaptive tuning of a Kalman filter via fuzzy logic has been one of the promising strategies to cope with divergence when dealing with non-white noise. The fuzzy logic adaptive controller (FLAC) will continually adjust the noise strengths in the filter's internal model and tune the filter. This paper presents a new INS/GPS sensor fusion scheme based on Fuzzy Adaptive Unscented Kalman Filter (FAUKF). The FAUKF is based on the combination of the unscented Kalman filter and the fuzzy logic controller which performs adaptation task for dynamic characteristics. Results obtained by FAUKF were compared to the Extended Kalman filter (EKF), Unscented Kalman Filter (UKF) and Fuzzy Adaptive Extended Kalman Filter (FAEKF). This comparative study has demonstrated that the FAUKF leads to very promising results as compared the other three Kalman filters.

Additional Metadata
Persistent URL dx.doi.org/10.1109/MMAR.2016.7575308
Conference 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016
Citation
Yazdkhasti, S. (Setareh), Sasiadek, J, & Ulrich, S. (2016). Performance enhancement for GPS/INS fusion by using a fuzzy adaptive unscented Kalman filter. In 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016 (pp. 1194–1199). doi:10.1109/MMAR.2016.7575308