Enhanced GNSS Multipath Map in Urban Canyon Using User Data Clouding
Yongjun Lee, Byungwoon Park, Sejong University
Date/Time: Thursday, Sep. 19, 2:12 p.m.
In urban environments, GNSS signals are prone to being blocked or reflected by dense tall buildings, resulting in a degradation of GNSS performance. Receiving reflected signals introduces distance errors known as multipath errors, which can result in position error of several hundred meters in urban canyon. Multipath erorrs are categorized as NLOS type multipath, where only reflected signals are received, or LOS type multipath, where both direct and reflected signals are received simultaneously. However, distinguishing between these two types of multipath in urban environment is very challenging, and modeling or predicting them is very difficult due to the site-dependent nature of these errors, which vary sensitively depending on the user's reception environment.
In our previous study, we proposed a method to model site-dependent multipath errors by constructing multipath maps in urban areas. The multipath maps, generated from machine learning-based multipath prediction model, provide range correction for all received signals without classifying between NLOS/LOS types of multipath and can be constructed using only GNSS data. However, to extract training data for multipath prediction model, a reference device is needed to calculate the accurate position of the data collection vehicle, requiring the use of specialized vehicles equipped with reference devices for initial multipath map construction or updating multipath map.
In this study, we propose a technique to automatically enhance multipath maps through user data-based clouding by combining multipath maps with CMC-based multipath estimation techniques. Application of this synergistic fusion of two technique to field test data showed that the position of a vehicle starting from the middle of an urban canyon can be obtained with a horizontal error of within 3 meters, even without initial position information. By utilizing the proposed technique on user data, it becomes possible to extract training data for update multipath maps, even if the data wasn’t collected by specialized vehicles equipped with reference device. Furthermore, the updating the multipath map through the data clouding not only enhances the map but also automatically reflect changes in the urban environment over time. The proposed techinique was applied to data collected in Seoul, resulting in a performance enhancement, with the 3D rms errors decreasing to 50m, 23m, and 19m, respectively, after two multipath map updates, demonstrating improvements of 53% and 19%.
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