Research On Application Of Linear Filtering Technique In The Global Navigation Satellite Systems

This thesis studies the nonlinear filtering technique in the Global Navigation Satellite System (GNSS) . It consists of four main parts:In the first part, we summarize the thesis. First we introduces the background of nonlinear filtering technique, and then we realize the current development status.In the second part, we study the nonlinear filtering algorithm. First, we review the extended kalman filter, and point out two drawbacks of the EKF. Then, aimed at the problems in the EKF, an algorithm adapted to the nonlinear systems called unscented kalman filter is introduced. And at last, we describe the method of error analysis.In the third part, we study the target motion model. At first, we introduce some common target motion models, and analyzes the advantages and disadvantages of them. Then, we study some turning motion model, and then puts forward a curve motion model and its brief model based on the particle. And at last, we describe the basic theory of multimodel estimate algorithm, and review the interactive multiple model algorithm chiefly.In the fourth part, we research the application of nonlinear filtering technique in the global navigation satellite system. Firstly, we establish the process model based on the target motion models which mentioned in the third part. Secondly, we briefly analyze the errors in the GNSS, and establish the observation model based on the pseudorange measurement. Thirdly, an ideal satellite orbit is simulated, and on this foundation, we establish some typical simulation and testing circumstances. Lastly, after simulation in the simulation and testing circumstances, we compare the performance of UKF and EKF. And then, based on the outdoor experiment of the vehicle, an analysis and contrast between our simulation results and commercial data process software is carried out, and the conclusion is obtained.By the simulation, it is indicated that the curve motion model based on the particle can describe the curve motion of the target. And it is shown that the performance of the UKF algorithm is more precise than the tradition nonlinear filtering technique. By the contrast between the simulation results and the positioning result of commercial data process software,...