Gps Navigation And Positioning Algorithm Research

GPS has been widely concerned on the military and the civilian since it was used, which has been developed rapidly. Currently, it is widely applied to many areas, such as aerospace, aviation, maritime, transport, surveying and so on, many of which are highly dynamic environments. The traditional least-squares algorithm with poor performance has the disadvantage of low positioning accuracy, so it can not meet the more stringent performance requirements.Kalman filtering is one of the estimation algorithms for random signals based on the theory of linear minimum variance estimation. It combines outdated measurement and real-time measurement using recursive algorithm, and efficiently estimates signals. Compared to the traditional least squares method, Kalman filtering algorithm can improve positioning accuracy, which is more suitable for dynamic data processing.Firstly, this thesis introduces the component of GPS and basic theory of positioning. Secondly, it models the moving of receiver, elicits Kalman Optimal Estimation, solves the common problems of filtering divagating and linearization of nonlinear systems, modifies the Kalman filtering and improves positioning precision and stability. Thirdly, it discusses error sources of GPS and impact on the positioning accuracy, analyzes how to improve positioning performance and researches Autonomous Integrity Monitoring of GPS. This thesis discusses how to detect and separate satellite malfunction based on the research of available RAIM algorithm, and a new method is put forward. Finally, it simulates the entire positioning process, compares the speed and positioning accuracy of least-squares algorithm and Kalman filtering algorithm. The proposed new RAIM algorithm is also simulated. The simulation result shows that RAIM algorithm is accurate and useful.