2:00 PM - 2:15 PM
[HTT21-02] Validation of Real-Time Precision Surveying Using Inexpensive Single-Frequency Type Local Area RTK System and Networks
Keywords:Low-cost Precise Survey System, Local-Area RTK-GNSS, Geographic Information System
Shinmura and Nasu (2020, 2021) constructed and verified an inexpensive and easy-to-handle local area RTK-GNSS surveying system and obtained stable data with an accuracy of less than a few centimeters. In this study, we extended this system to automatically perform RTK surveying every second to verify the accuracy and reliability of real-time data. The surveying was conducted at a location where there were obstacles to GNSS satellite reception such as buildings and trees on the university campus (ground fixed point), which was used in Shinmura and Nasu (2021), and at the roof of an 11-story school building (rooftop fixed point) where a reference station had already been set up and the sky was open and there were very few obstacles to GNSS satellite reception. At the ground station, the network connection status, temperature and relative humidity were measured simultaneously. 600 data were acquired in 10 minutes, and the data with the highest ratio were selected for analysis.
In both ground and rooftop fixed points, in the distribution of the difference between the representative value and the value determined as the most concentrated value of the data, there was a gap of about 3.0×10-7 degrees where the distribution became less. After evaluating the data quality using this as the boundary between normal and missed fix values, we explored the causes of the quality decline and found a way to improve the overall quality at the data processing stage. The quality of the overall data was evaluated in two ways: the size of the standard deviation to determine the variability, and whether the representative values fell within the error range of two times the standard error. For ground-based fixed points, the standard error of all data was more than 10 times more accurate than that of D-GNSS, but this was inadequate as expected for RTK surveying. By filtering the data with less than 15 satellites and less than 9 ratio values, the data quality and reliability were improved to a satisfactory level. For the rooftop fixed point, the standard error of all data was about 1mm in both horizontal and height directions, which was much more accurate than the data from the ground fixed point, and we could obtain highly accurate and reliable data with less variability. By filtering the data in a similar way, it was possible to obtain accurate RTK survey data in real time without degrading the quality of the data. This result shows that it is possible to obtain accurate and reliable RTK survey data in real time by using statistical processing to eliminate outliers. These results may be applied to automatic driving of automobiles, automatic flight of drones, and ground deformation monitoring systems.
In both ground and rooftop fixed points, in the distribution of the difference between the representative value and the value determined as the most concentrated value of the data, there was a gap of about 3.0×10-7 degrees where the distribution became less. After evaluating the data quality using this as the boundary between normal and missed fix values, we explored the causes of the quality decline and found a way to improve the overall quality at the data processing stage. The quality of the overall data was evaluated in two ways: the size of the standard deviation to determine the variability, and whether the representative values fell within the error range of two times the standard error. For ground-based fixed points, the standard error of all data was more than 10 times more accurate than that of D-GNSS, but this was inadequate as expected for RTK surveying. By filtering the data with less than 15 satellites and less than 9 ratio values, the data quality and reliability were improved to a satisfactory level. For the rooftop fixed point, the standard error of all data was about 1mm in both horizontal and height directions, which was much more accurate than the data from the ground fixed point, and we could obtain highly accurate and reliable data with less variability. By filtering the data in a similar way, it was possible to obtain accurate RTK survey data in real time without degrading the quality of the data. This result shows that it is possible to obtain accurate and reliable RTK survey data in real time by using statistical processing to eliminate outliers. These results may be applied to automatic driving of automobiles, automatic flight of drones, and ground deformation monitoring systems.