DAI Wujiao, WU Xixiu, LUO Feixue. GPS Multipath Effect Processing Method Based on Augmented Parameters Kalman Filtering[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 423-427.
Citation:
DAI Wujiao, WU Xixiu, LUO Feixue. GPS Multipath Effect Processing Method Based on Augmented Parameters Kalman Filtering[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 423-427.
DAI Wujiao, WU Xixiu, LUO Feixue. GPS Multipath Effect Processing Method Based on Augmented Parameters Kalman Filtering[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 423-427.
Citation:
DAI Wujiao, WU Xixiu, LUO Feixue. GPS Multipath Effect Processing Method Based on Augmented Parameters Kalman Filtering[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 423-427.
1 Department of Survey Engineering and Geomatics,Central South University,932 South Lushan Road,Changsha 410083,China;2 Key Laboratory of Precise Engineering Surveying & Deformation Disaster Monitoring of Hunan Province, 932 South Lushan Road,Changsha 40083,China
In the high-precision positioning with GPS,when the environment of the surveying point keep little changed,the multipath effect has strong repeatability.With this characteristic establishing the error correction model is an effective method to weaken the multipath effect influence.But as the time interval goes on,its repeatability decreases,and the corresponding effectiveness of this method drastically reduces.Therefore,based on augmented parameters Kalman filtering this paper proposes a multipath effect system error estimation method with state matrix augmentation,taking the systematic errors as state parameters and establishing AR model of first class,meanwhile using multipath repeatability characteristics to update multipath error correction model.With this method,the problem of that as the time goes on the repeatability decrease and the corresponding effectiveness of fix multipath error correction model reduces has been resolved to a certain extent.Finally,an example with 16 days GPS observation data has proved that this method has a certain feasibility and effectiveness.
Shao Jianping, Shan Heming, Song Guangpu. The Kalman Filtering to Counteract SA[J]. Geomatics and Information Science of Wuhan University, 1994, 19(3): 274-279.
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