A modified un-combined model to improve the performance of precise point positioning: model and test results

Zhao, Xin; Liu, Chao; Deng, Jianan; Cui-ying, Zhang; Yu, Xuexiang

doi:10.1007/s40328-016-0175-3

Cited by 2 publications

(2 citation statements)

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“…There are many observation models developed for PPP, including the ionosphere-free (IF) combination model, Uofc model, un-combined model [1,[16][17][18]. Because the IF combination model can eliminate nearly all of the ionospheric propagation delays by a combination of dualfrequency data with a simple model, it has been widely used as the observation model for PPP [19].…”

Section: Basic Models Of Precise Point Positioningmentioning

confidence: 99%

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Metrology and Measurement Systems

Zhang¹,

Zhao²,

Zhou³

2019

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The accuracy and reliability of Kalman filter are easily affected by the gross errors in observations. Although robust Kalman filter based on equivalent weight function models can reduce the impact of gross errors on filtering results, the conventional equivalent weight function models are more suitable for the observations with the same noise level. For Precise Point Positioning (PPP) with multiple types of observations that have different measuring accuracy and noise levels, the filtering results obtained with conventional robust equivalent weight function models are not the best ones. For this problem, a classification robust equivalent weight function model based on the t-inspection statistics is proposed, which has better performance than the conventional equivalent weight function models in the case of no more than one gross error in a certain type of observations. However, in the case of multiple gross errors in a certain type of observations, the performance of the conventional robust Kalman filter based on the two kinds of equivalent weight function models are barely satisfactory due to the interaction between gross errors. To address this problem, an improved classification robust Kalman filtering method is further proposed in this paper. To verify and evaluate the performance of the proposed method, simulation tests were carried out based on the GPS/BDS data and their results were compared with those obtained with the conventional robust Kalman filtering method. The results show that the improved classification robust Kalman filtering method can effectively reduce the impact of multiple gross errors on the positioning results and significantly improve the positioning accuracy and reliability of PPP.

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Section: Basic Models Of Precise Point Positioningmentioning

confidence: 99%

“…The conventional equivalent weight function model described by (18) is constructed by the standardized residual statistics and the critical values are often established by experience. It has a good performance for observations with the same accuracy and has been verified and widely used.…”

Section: Conventional Robust Equivalent Weight Function Modelmentioning

confidence: 99%