| The fusion of Global Navigation Satellite System(GNSS)and Inertial Navigation System(INS)has been the most widely used in vehicle positioning.As the main information source in the vehicle positioning,the positioning performance of Global Navigation Satellite System(GNSS)is severely degraded in urban canyons due to the effects of Non-Line-Of-Sight(NLOS)and multipath propagations.How to reduce the negative effects and achieve accurate positioning performance in urban canyons is a challenging and significant issue.To deal with this problem,the fusion positioning solution for land vehicle in urban canyons is researched in this paper.The main research works are summarized as follows:(1)The multi-sensor collection software has been designed and constructed,which can realize synchronous sampling,saving,and processing of multi-sensors’ data.(2)The positioning model is established based on the direct and tightly-coupled integration of a cost-effective Reduced Inertial Sensor system(RISS)and dual-constellation GNSSs.(3)A sample but efficient 3D building map is designed and constructed,and a satelliteselection method is designed to exclude the NLOS signals with aid of the 3D building map.Moreover,the fuzzy logic is designed and introduced to adaptively adjust the dependence on each received satellite measurement in urban areas.(4)The Enhanced Extended Kalman particle filter(Enhanced-EKPF)algorithm,which integrates the satellite-selection method and the fuzzy logic with the EKPF algorithm,is developed to execute the global fusion,in order to mitigate the NLOS and multipath interferences and achieve accurate positioning in urban canyons.At present,the project has completed the work above.The proposed solution is evaluated through experiments.The results validate that the proposed low-cost tightly-coupled positioning solution based on Enhanced-EKPF can effectively reduce the positioning errors caused by the NLOS and multipath signals and achieve accurate vehicle position in urban canyons. |
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