Research On Satellite Selection Algorithm And Analysis Of Global Navigation Performance For Dual Mode System

When faced with harsh environments,performance of the single navigation system performs poorly and has limited positioning accuracy.With development and improvement of the satellite navigation systems,compared with single navigation system,integrated navigation systems have received more attention because they can provide more visible satellites and improve positioning accuracy.Meanwhile,the latest completion of Beidou-3system provides new impetus for the development of integrated navigation systems.However,in the scenario of satellite selection in integrated system,the increase of redundant satellite signals increases the time and computational cost of satellite selection.Therefore,in order to improve the problem of satellite selection,this thesis proposes satellite selection algorithms based on differential evolution,which use the fast search ability of differential evolution to achieve the purpose of fast satellite selection.The main research work and innovations of this thesis are as follows:Based on the dual-mode integrated navigation system with the latest Beidou-3 system,sufficient satellite observation stations are set up around the world to verify and analyze the navigation performance advantages of the integrated navigation system,providing a reference for the application of the Beidou-3 system and its integrated systems.In order to solve the problem that the accuracy of geometric dilution of precision is not high due to excessive control parameters or difficult control in the application of existing intelligent optimization algorithms,a satellite selection scheme based on standard differential evolution algorithm is proposed,and excellent mutation mode and control parameter range with better performance is selected.Simulation experiments show that,compared with the particle swarm optimization algorithm,the standard differential evolution algorithm with fixed parameters has higher accuracy of geometric dilution of precision,but the time-cost is also increased.Subsequently,the self-adaptive parameter scheme is adopted to flexibly change the control parameters to adapt to the changing environment of evolution.The experimental results show that,compared with the standard differential evolution algorithm,the differential evolution algorithm with self-adaptive scheme has a higher accuracy of geometric dilution of precision,but it still brings a certain increase in the time-cost of satellite selection.In order to further improve the time-cost problem of satellite selection,a kind of geometric distribution model was constructed.Based on the self-adaptive parameters and geometric distribution model,hybrid differential evolution satellite selection algorithm is proposed.The experimental results show that,compared with the standard differential evolution satellite selection algorithm and particle swarm optimization satellite selection algorithm,the hybrid differential evolution algorithm can ensure relatively good accuracy of the geometric dilution of precision and make further decrease in the time-cost of satellite selection at the single station and multiple stations.The algorithm provides another effective solution for the satellite selection process of the integrated system.