Accurate differential global positioning system via fuzzy logic Kalman filter sensor fusion technique

Abstract

The ability to determine an accurate global position of a vehicle has many useful commercial and military applications. The differential global positioning system (DGPS) is one of the practical navigation tools used for this purpose. However, the DGPS has limitations arising from slow updates, signal interference, and limited accuracy. This paper describes how vehicle rate sensors ran be used to help a DGPS overcome these limitations. The theoretical background for the sensor fusion is based on the principle of Kalman filtering and a fuzzy logic scheme. Validity of the method was verified by using experimental data from an actual automobile navigating around an urban area. The results demonstrated that the path of the automobile can be continuously traced with high accuracy and repeatability, in spite of the limitations of the DGPS.