Enhanced Map-Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons

Abstract

To realize the effective positioning in urban canyons, an enhanced map-aided Global Positioning System (GPS)/three-dimensional (3D) reduced inertial sensor system (RISS) tightly combined positioning strategy is proposed. First, the 3D RISS is only based on the built-in controller area network (CAN). CAN bus sensor without additional sensors is first constructed to lower the cost. Then, a simple but effective enhanced map is created to assist positioning. Based on the map, a Kalman filtering (KF) tightly coupled method is proposed to fuse the 3D RISS with GPS information and to achieve the preliminary positioning. In KF-based preliminary positioning method, a simply observation noise variance optimization algorithm based on 2D enhanced map is proposed to improve KF method. In this algorithm, the value of the observation noise variance matrix is determined only according to the building plane information which is contained in the enhanced map. Further, a multiweight map matching algorithm is proposed for optimizing the initial positioning results. In this algorithm, factors such as distance, direction, road network topology, and lane change are considered and applied to map matching to further increase the positioning performance and form the final positioning results. Finally, the effectiveness of the strategy is proved by field test. The results show that this method has better accuracy and reliability than the conventional method.