Nonlinear Filtering Theory And Application In Multipath Mitigation For GNSS

Multipath is known to be one of the most dominant sources of accuracy degradation in Global Navigation Satellite System, especially in high-precision positioning applications. It's too difficult to mitigation this error in satellite navigation system. However, with the development of the nonlinear filtering theory, it provides a new approach to solve this issue. In the thesis, we research the multipath mitigation problem base on nonlinear filtering theory. The main contributions are as follows,Firstly, we take the research on nonlinear filtering theory under the Bayesian framework. In that case, Kalman filter proves to be the optimal Bayesian filter with the linear Gaussian assumption. And in actually, the nonlinear Kalman filter can be considered as a linear approximation of the optimal Bayesian filter. The performances of EKF and UKF algorithms are compared by simulations. Furthermore, particle filters are considered in this thesis. To solve the problem of particle degeneracy, an improved algorithm is employed in particle filter. Simulation results that the new method is more efficient, accuracy and stable.Secondly, we analyze the characters of multipath error in different modulations, such as L1 in GPS and E1 in Galileo system. It results that, (1)Multipath error comes form multipath signal which affects the output of the discriminator in receiver by changing the shape of auto-correlation function of PRN codes. (2)In-phased multipath error is different from that of out-phased which is more harmful to DLL. (3)The multipath error can be mitigation by reducing the distance of correlators in discriminator.(4)It is different for multipath error in different modulations. For example, BOC modulation is much better than BPSK modulation in resisting multipath interference. At last, the multipath mitigation problem is considered. We first investigate the delay modeling problem together with the associated estimation theory of Maximum Likelihood Principle. Then the numerical method of Newton is derivative in analytical form to ensure the convergence and efficiency of the iteration formula. To exploit the nonlinear filtering theory in multipath mitigation problem, we set up the discrete state-space model and then design a nonlinear filtering algorithm based on state-space decomposition. At the end of the discussion, we talk about the performance of ML method and nonlinear filtering method in multipath mitigation problem. Simulations show that nonlinear filtering method is better than ML method in the case of varying delay.