Research On Multipath Errors Mitigation Algorithm In GPS

Global positioning system(GPS)is widely used in people's lives,and with the increasing demand for high precision navigation system,interference suppression has become a research hotspot.Among the many factors that affect the positioning accuracy,multipath interference is one of the main sources of error.The difficulty of multipath error suppressing is due to the irrelevance of position and the uncertainty of time for multipath generation.Multipath error can not be eliminated by the existing differential techniques.And the multipath error suppression method based on data processing is in accordance with the development trend of the current software receiver.Therefore,the multipath error suppression algorithms based on parameter estimation are studied.It aims to estimate multipath parameters by data processing and reconstruct multipath signal,and thus eliminate the impact of multipath interference,in order to achieve the purpose of suppressing multipath error.This paper focuses on the multipath error suppression algorithms under Gaussian noise and non-Gaussian noise.Extended Kalman Filter(EKF)and Particle Filter(PF)are two typical algorithms for multipath estimation under Gaussian noise and non-Gaussian noise.Under Gaussian noise,there are two main disadvantages for multipath parameters estimation by using the EKF: 1)It is sensitive to the initial value;2)it produces a truncation error in the linearization of a nonlinear equation,and the filtering results fluctuate obviously around true values.Under non-Gaussiannoise,the PF algorithm is widely used and the filtering effect is acceptable.However,the standard PF has the problem of particle impoverishment in parameter estimation,resulting in the reduction of particle diversity,which causes the performance degradation of PF algorithm.To solve these problems that EKF is sensitive to the initial value and filtering results fluctuate obviously around actual values,an improved multipath estimation algorithm based on PF and sliding average EKF is proposed.Firstly,PF is used to obtain rough estimation values of multipath parameters,which are set as initial estimations for EKF to reduce the initial value sensitivity.Then,the EKF filtering results are smoothed by sliding average.The smoothing results are outputted as the multipath estimation.The simulation results show that the estimation results of the proposed algorithm have smaller fluctuation magnitude compared with EKF,and it is insensitive to the initial estimation.The impoverishment of samples in PF is a problem studied by many researchers.To solve this problem,a new PF algorithm based on Adaptive Differential Evolution(ADE)is proposed,named as ADE-PF.In ADE-PF,the ADE algorithm instead of the re-sampling strategy is used to generate new particles in PF,which promotes the particles moving toward the region with high likelihood in the state posterior probability density function,and increases the diversity of the particles in PF.In addition,a nonlinear adaption control strategy is adopted to adjust the mutation factor and the crossover factor in DE.Furthermore,in order to verify the effectivity of the proposed algorithm,the ADE-PF is applied for multipath estimationIn under Gaussian noise environment and non-Gaussian noise environment.The simulation results show that ADE-PF is a feasible method for solving the problem of particle impoverishment in PF,and it outperforms PF and EKF for multipath estimation.The research of this paper is an important part of the Natural Science Foundation of Shanxi Province(No.2014021022-7),which provides a reference for the study of multipath error suppression under Gaussian noise andnon-Gaussian noise.It has important theoretical significance and wide application prospect for improving the positioning accuracy of navigation system.