The discrimination test of ambiguity resolution,also known as ambiguity validation,is a vital procedure to quantify the reliability of Global Navigation Satellite System(GNSS)ambiguity-fixed solutions.Several well-kno...The discrimination test of ambiguity resolution,also known as ambiguity validation,is a vital procedure to quantify the reliability of Global Navigation Satellite System(GNSS)ambiguity-fixed solutions.Several well-known tests,including the R-ratio,W-ratio,and Ambiguity Dilution of Precision,usually employ empirical thresholds for the discrimination of integer candidates.We aim at improving the reliability of ambiguity validation by integrating these tests using a machine learning model called the Support Vector Model(SVM).The dataset used consists of simulated real-time Precise Point Positioning Ambiguity Resolution(PPP-AR)solutions in 1-day batch.Specifically,the training dataset is derived using the observations from days 1-31 of year 2023,while the testing dataset is generated using the observations from days 153-159 of years 2022 and 2024.The results reveal that the SVM validates PPP-AR at a success rate of 83%for the independent testing dataset.At the same time,the mean error of the convergence time predicted by the SVM is about 1.0 min,whereas that by the R-ratio test up to 5.0 min.A vehicle-borne experiment conducted on day 362 of year 2020 further demonstrates the improvement of this method in a kinematic scenario,with a success rate of 92%compared to 82%with the conventional R-ratio test.展开更多
Navigation system integrity monitoring is crucial for mission(e.g.safety)critical applications.Receiver autonomous integrity monitoring(RAIM)based on consistency checking of redundant measurements is widely used for m...Navigation system integrity monitoring is crucial for mission(e.g.safety)critical applications.Receiver autonomous integrity monitoring(RAIM)based on consistency checking of redundant measurements is widely used for many applications.However,there are many challenges to the use of RAIM associated with multiple constellations and applications with very stringent requirements.This paper discusses two positioning techniques and corresponding integrity monitoring methods.The first is the use of single frequency pseudorange-based dual constellations.It employs a new cross constellation single difference scheme to benefit from the similarities while addressing the differences between the constellations.The second technique uses dual frequency carrier phase measurements from GLONASS and the global positioning system for precise point positioning.The results show significant improvements both in positioning accuracy and integrity monitoring as a result of the use of two constellations.The dual constellation positioning and integrity monitoring algorithms have the potential to be extended to multiple constellations.展开更多
基金funded by National Science Foundation of China(No.42025401)the Projects of International Cooperation and Exchanges NSFC(42361134580,42311530062).
文摘The discrimination test of ambiguity resolution,also known as ambiguity validation,is a vital procedure to quantify the reliability of Global Navigation Satellite System(GNSS)ambiguity-fixed solutions.Several well-known tests,including the R-ratio,W-ratio,and Ambiguity Dilution of Precision,usually employ empirical thresholds for the discrimination of integer candidates.We aim at improving the reliability of ambiguity validation by integrating these tests using a machine learning model called the Support Vector Model(SVM).The dataset used consists of simulated real-time Precise Point Positioning Ambiguity Resolution(PPP-AR)solutions in 1-day batch.Specifically,the training dataset is derived using the observations from days 1-31 of year 2023,while the testing dataset is generated using the observations from days 153-159 of years 2022 and 2024.The results reveal that the SVM validates PPP-AR at a success rate of 83%for the independent testing dataset.At the same time,the mean error of the convergence time predicted by the SVM is about 1.0 min,whereas that by the R-ratio test up to 5.0 min.A vehicle-borne experiment conducted on day 362 of year 2020 further demonstrates the improvement of this method in a kinematic scenario,with a success rate of 92%compared to 82%with the conventional R-ratio test.
文摘Navigation system integrity monitoring is crucial for mission(e.g.safety)critical applications.Receiver autonomous integrity monitoring(RAIM)based on consistency checking of redundant measurements is widely used for many applications.However,there are many challenges to the use of RAIM associated with multiple constellations and applications with very stringent requirements.This paper discusses two positioning techniques and corresponding integrity monitoring methods.The first is the use of single frequency pseudorange-based dual constellations.It employs a new cross constellation single difference scheme to benefit from the similarities while addressing the differences between the constellations.The second technique uses dual frequency carrier phase measurements from GLONASS and the global positioning system for precise point positioning.The results show significant improvements both in positioning accuracy and integrity monitoring as a result of the use of two constellations.The dual constellation positioning and integrity monitoring algorithms have the potential to be extended to multiple constellations.
