Assessment Accuracy of Standard Point Positioning Enhanced by Observation and Position Domain Filtering Utilizing a Multi-Epoch Least-Squares Integration Method
-
Published:2024-01-29
Issue:3
Volume:16
Page:517
-
ISSN:2072-4292
-
Container-title:Remote Sensing
-
language:en
-
Short-container-title:Remote Sensing
Author:
Li Fangchao12, Psimoulis Panos3ORCID, Li Qi45, Yang Jie1, Gao Jingxiang2, Kou Xiaomei45, Niu Le45, Meng Xiaolin6ORCID
Affiliation:
1. College of Forestry (Academy of Forestry), Northwest A&F University, Yangling 712100, China 2. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China 3. Nottingham Geospatial Institute, The University of Nottingham, Nottingham NG7 2TU, UK 4. Power China Northwest Engineering Corporation Limited, Xi’an 710065, China 5. Shaanxi Union Research Center of University and Enterprise for River and Lake Ecosystems Protection and Restoration, Xi’an 710065, China 6. Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Abstract
To enhance the positioning accuracy of standalone GNSS receivers in environments unable to provide precise ephemeris and clock offset, such as undeveloped forest areas that lack network communication and power supply, this study employed the Time Difference Carrier Phase (TDCP) technology to improve the positioning accuracy of Standard Point Positioning (SPP), where the Least-Squares (LS) and the extended Multi-Epoch Least Squares (MELS) method were applied in the position domain filtering for a single GNSS receiver and compare its performance with the existing observation domain filtering method. Firstly, the simulated data sets with various positioning accuracies were used to verify the effectiveness and convergence of the LS filtering methods. The results indicate that the LS filtering method produces a lower root mean square (RMS) error than the original strategy. Secondly, this study uses two kinematic GNSS data sets to evaluate the performance of the observation and position domain filtering, with an emphasis on the MELS method. The numerical experiment results show that the position domain LS filtering method outperforms the other two methods. The open environment experiments result shows that the positioning domain filtering method achieved positioning accuracies of 0.202 m, 0.843 m, and 2.036 m in the E, N, and U directions, respectively, with improvements of 68.0%, 21.6%, and 24.0%, compared to the original algorithm which achieved positioning accuracies of 0.631 m, 1.076 m, and 2.680 m. It also achieved improvements of 24.0%, 4.0%, and 18.3%, respectively, compared to the observation domain filtering method with positioning accuracies of 0.353 m, 0.886 m, and 2.526 m. The forest scenes experiments result shows that the positioning domain filtering method achieved positioning accuracies of 1.308 m, 1.375 m, and 2.133 m in the E, N, and U directions, respectively, with improvements of 42.4%, 36.2%, and 27.6%, compared to original algorithm which achieved positioning accuracies of 1.863 m, 1.873 m, and 2.722 m, and also achieved improvements of 27.0%, 19.4% and 10.6%, respectively, comparing to observation domain filtering method with positioning accuracies of 1.661 m, 1.642 m and 2.359 m. Moreover, the examination of the LS method results based on different epochs reveals that the filtering accuracy increases as more epochs are incorporated into the position domain integration and the enhancement value reaches a few millimeters.
Funder
National Key Research and Development Program of China Natural Science Basic Research Program of Shaanxi Chinese Universities Scientific Fund Shaanxi Provincial Education Department Shaanxi Provincial Philosophy and Social Science Research Project National Science Foundation of China
Reference57 articles.
1. Lee, T., Bettinger, P., Merry, K., and Cieszewski, C. (2023). The effects of nearby trees on the positional accuracy of gnss receivers in a forest environment. PLoS ONE, 18. 2. Feng, T., Chen, S., Feng, Z., Shen, C., and Tian, Y. (2021). Effects of canopy and multi-epoch observations on single-point positioning errors of a gnss in coniferous and broadleaved forests. Remote Sens., 13. 3. Guo, J., Hou, R., Zhou, M., Jin, X., Li, C., Liu, X., and Gao, H. (2021). Monitoring 2019 forest fires in southeastern australia with gnss technique. Remote Sens., 13. 4. Improving the precision and accuracy of wildlife monitoring with multi-constellation, multi-frequency gnss collars;Azorit;J. Wildl. Manag.,2023 5. Accuracy of kinematic positioning using global satellite navigation systems under forest canopies;Kaartinen;Forests,2015
|
|