Abstract. Kalman filters have been widely used for navigation and system integration. One of the key problems associated with Kalman filters is how to assign suitable statistical properties to both the dynamic and the observational models. For GPS navigation, the manoeuvre of the vehicle and the level of measurement noise are environmental dependent, and hardly to be predicted. Therefore to assign constant noise levels for such applications is not realistic. In this paper, real-time adaptive algorithms are applied to GPS data processing. Two different adaptive algorithms are discussed in the paper. A number of tests have been carried out to compare the performance of the adaptive algorithms with a conventional Kalman filter for vehicle navigation. The test results demonstrate that the new adaptive algorithms are much robust to the sudden changes of vehicle motion and measurement errors.
Note that the GPS positions can be sophisticated by applying Adaptive Kalman Filtering REF ), which is more robust to the sudden changes of moving motion and measurement errors than the conventional Kalman Filtering.
Adaptive Kalman Filtering for Vehicle Navigation
Journal of Global Positioning Systems
Engineering