Research On Multipath Suppression Algorithm Based On Parameter Estimation In Navigation System

As the global navigation satellite system(GNSS)has become more widely used,users have put forward higher and higher requirements for its positioning accuracy.Therefore,the error elimination technology in the navigation system has become a research hotspot.Among the many errors that affect positioning accuracy,multipath error is one of the most important factors that reduce positioning accuracy.Multipath error is a kind of accidental error.It changes with the surrounding environment of the signal,making it irrelevant in time and uncertain in position,which makes it impossible to eliminate by differential technology.Therefore,many scientific researchers have done a lot of research on multipath suppression in navigation systems,making the multipath suppression technology based on different principles continue to emerge.These include: multipath suppression based on the receiver antenna,receiver tracking loop and multipath suppression based on baseband digital processing.Due to the high cost of improvement based on the antenna end and receiver tracking loop,this article mainly proposes two multipath error suppression algorithms based on digital processing technology to suppress multipath interference:(1).In order to solve the problems of particle degradation and particle diversity in particle filter(PF)algorithm parameter estimation in static environment,a new algorithm for PF optimization is proposed,which combines unscented kalman filter(UKF)algorithm with improved differential evolution(IDE)algorithm.Firstly,aiming at the problem that differential evolution(DE)is easy to fall into the local optimal solution,an adaptive strategy is adopted in the process of mutation and crossover to continuously adjust the mutation factor and crossover factor to meet the needs of DE algorithm in different stages.Secondly,the UKF algorithm is "embedded" into the importance sampling process of the PF algorithm,and the improved DE algorithm is used to replace the re-sampling process of the PF algorithm to improve the parameter estimation accuracy of the PF algorithm.Finally,in order to verify the effectiveness of the improved algorithm,it is used for multipath parameter estimation in a static environment,and the simulation results are analyzed from the three performance indicators of fluctuation range,average error and correlation function,and the improved algorithm is obtained.Compared with PF algorithm,UPF algorithm and the comparison algorithm mentioned in the literature,it has better performance in the process of multipath suppression.(2).In a dynamic environment,due to the complexity of the actual environment,the generation and disappearance of multipath in the received signal are always random,which makes it difficult to achieve multipath parameter estimation.The adaptive filtering algorithm is widely used in the field of parameter estimation because of its self-learning function.However,the convergence speed and steady-state error of the traditional adaptive filtering algorithm based on least mean square(LMS)restrict each other in the iterative process,aiming at this problem,this paper proposes an improvement least mean square(ILMS)adaptive filtering algorithm to achieve multipath suppression.The algorithm constructs a non-linear function expression to replace the constant value parameters existing in the traditional LMS adaptive filter algorithm,so that it can ensure a small steady-state error under the condition of a faster convergence rate,thereby satisfying the adaptive filter algorithm Requirements for convergence speed and steady-state performance.Finally,the ILMS adaptive filtering algorithm is compared with the existing LMS adaptive filtering algorithm,SVSLMS algorithm and G-SVSLMS algorithm.The simulation results show that the ILMS adaptive filtering algorithm can achieve better results than the other three adaptive filtering algorithms in the multipath suppression process.