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http://hdl.handle.net/10397/93339
Title: | Improving GPS code phase positioning accuracy in urban environments using machine learning | Authors: | Sun, R Wang, G Cheng, Q Fu, L Chiang, KW Hsu, LT Ochieng, WY |
Issue Date: | 15-Apr-2021 | Source: | IEEE internet of things journal, 15 Apr. 2021, v. 8, no. 8, p. 7065-7078 | Abstract: | The accuracy of location information, mainly provided by the global positioning system (GPS) sensor, is critical for Internet-of-Things applications in smart cities. However, built environments attenuate GPS signals by reflecting or blocking them resulting in some cases multipath and non-line-of-sight (NLOS) reception. These effects cause range errors that degrade GPS positioning accuracy. Enhancements in the design of antennae and receivers deliver a level of reduction of multipath. However, NLOS signal reception and residual effects of multipath are still to be mitigated sufficiently for improvements in range errors and positioning accuracy. Recent machine learning-based methods have shown promise in improving pseudorange-based position solutions by considering multiple variables from raw GPS measurements. However, positioning accuracy is limited by low accuracy signal reception classification. Unlike the existing methods, which use machine learning to directly predict the signal reception classification, we use a gradient boosting decision tree (GBDT)-based method to predict the pseudorange errors by considering the signal strength, satellite elevation angle and pseudorange residuals. With the predicted pseudorange errors, two variations of the algorithm are proposed to improve positioning accuracy. The first corrects pseudorange errors and the other either corrects or excludes the signals determined to contain the effects of multipath and NLOS signals. The results for a challenging urban environment characterized by high-rise buildings on one side, show that the 3-D positioning accuracy of the pseudorange error correction-based positioning measured in terms of the root mean square error is 23.3 m, an improvement of more than 70% over the conventional methods. | Keywords: | Global positioning system (GPS) Gradient boosting decision tree (GBDT) Multipath Non-line-of-sight (NLOS) Urban positioning |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE internet of things journal | EISSN: | 2327-4662 | DOI: | 10.1109/JIOT.2020.3037074 | Rights: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication R. Sun et al., "Improving GPS Code Phase Positioning Accuracy in Urban Environments Using Machine Learning," in IEEE Internet of Things Journal, vol. 8, no. 8, pp. 7065-7078, 15 April15, 2021 is available at https://dx.doi.org/10.1109/JIOT.2020.3037074. |
Appears in Collections: | Journal/Magazine Article |
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Hsu_Improving_Gps_Code.pdf | Pre-Published version | 5.28 MB | Adobe PDF | View/Open |
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