The DGPS technique can provide considerably better relative positioning accuracy than the stand_alone GPS positioning,but the improvement depends on the distance between the user and the reference station (spatial cor...The DGPS technique can provide considerably better relative positioning accuracy than the stand_alone GPS positioning,but the improvement depends on the distance between the user and the reference station (spatial correlation),the latency of differential corrections (temporal correlation),and the quality of differential corrections.Therefore,how to correctly generate differential corrections as well as their pricision is very important to the DGPS positioning technique.This paper presents a new algorithm for generating differential GPS corrections.This algorithm directly uses code and carrier observations in the measurement model of a Kalman filter,so that it is possible to use a simple stochastic model and to use the standard algorithm of the Kalman filter.The algorithm accounts for biases like multipath errors and instrumental delays in code observations and it shows how differential corrections are differently affected by code biases when dual or single frequency data is used.In addition,the algorithm can be integrated with a real time quality control procedure.As a result,the quality of differential corrections can be guaranteed with a certain probability.展开更多
文摘The DGPS technique can provide considerably better relative positioning accuracy than the stand_alone GPS positioning,but the improvement depends on the distance between the user and the reference station (spatial correlation),the latency of differential corrections (temporal correlation),and the quality of differential corrections.Therefore,how to correctly generate differential corrections as well as their pricision is very important to the DGPS positioning technique.This paper presents a new algorithm for generating differential GPS corrections.This algorithm directly uses code and carrier observations in the measurement model of a Kalman filter,so that it is possible to use a simple stochastic model and to use the standard algorithm of the Kalman filter.The algorithm accounts for biases like multipath errors and instrumental delays in code observations and it shows how differential corrections are differently affected by code biases when dual or single frequency data is used.In addition,the algorithm can be integrated with a real time quality control procedure.As a result,the quality of differential corrections can be guaranteed with a certain probability.
