Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-22T16:19:54.753Z Has data issue: false hasContentIssue false

Enabling robust and accurate navigation for UAVs using real-time GNSS precise point positioning and IMU integration

Published online by Cambridge University Press:  19 October 2020

C. Chi
Affiliation:
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China
X. Zhan*
Affiliation:
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China
S. Wang
Affiliation:
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China
Y. Zhai
Affiliation:
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China

Abstract

Accurate navigation is required in many Unmanned Aerial Vehicle (UAV) applications. In recent years, GNSS Precise Point Positioning (PPP) has been recognised as an efficient approach for providing precise positioning services. In contrast to the widely used Real-Time Kinematic (RTK), PPP is independent of reference stations, which greatly broadens its scope of application. However, the accuracy and reliability of PPP can be significantly decreased by poor GNSS satellite geometry and outage. In response, a real-time four-constellation GNSS PPP is applied to improve the geometry in this work, and PPP is tightly coupled with an Inertial Measurement Unit (IMU) to smooth the position and velocity output, thus improving the robustness of the navigation solution. Experimental flight tests are carried out using a UAV in an open-sky area, and GNSS-challenged environments are simulated. The results show that the four-constellation GNSS PPP/IMU integration reduces the Root-Mean-Square (RMS) Three-Dimensional (3D) positioning and velocity error by 76.4% and 67.1%, respectively, in open sky with respect to the one-GNSS PPP. Under scenarios where GNSS measurements are insufficient, the coupled system can still provide continuous solutions. Moreover, the coupled PPP/IMU system can also maintain the convergence of PPP during GNSS-challenged periods and can greatly shorten the re-convergence period of PPP when the UAV returns to the open sky.

Type
Research Article
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Odijk, D., Verhagen, S. and Teunissen, P.J.G. Medium-distance GPS ambiguity resolution with controlled failure rate, Geodesy for Planet Earth, Berlin/Heidelberg, Germany, vol. 136, 2012, pp 745751.Google Scholar
Gezici, S., Tian, Z., Giannakis, G.B. et al. Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks, IEEE Sig Process Mag, 2005, 22, (4), pp 7084.CrossRefGoogle Scholar
Delmerico, J. and Scaramuzza, D. A benchmark comparison of monocular visual-inertial odometry algorithms for flying robots, 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, 2018, pp 25022509.CrossRefGoogle Scholar
Zumberge, J.F., Heflin, M.B., Jefferson, D.C., Watkins, M.M. and Webb, F.H. Precise point positioning for the efficient and robust analysis of GPS data from large networks, J Geophys Res, March 1997, 102, (B3), pp 50055017.CrossRefGoogle Scholar
Kouba, J. and Hroux, P.J.G.S. Precise point positioning using IGS orbit and clock products, GPS Solut, October 2001, 5, (2), pp 1228.CrossRefGoogle Scholar
Gao, Z., Li, Y., Zhuang, Y., Yang, H., Pan, Y. and Zhang, H. Robust Kalman filter aided GEO/IGSO/GPS raw-PPP/INS tight integration, Sensors, 2019, 19, (2), p 417.CrossRefGoogle ScholarPubMed
Laurichesse, D. and Mercier, F.J.E.G.A. Real-time PPP with undifferenced integer ambiguity resolution, experimental results, The 23nd International Technical Meeting of Satellite Division of the Institute of Navigation (ION GNSS 2010), 2010, vol. 7672, (6), p 8801.Google Scholar
Caissy, M., Weber, G., Agrotis, L., Wübbena, G. and Hernandez-Pajares, Manuel. The IGS real-time pilot project the development of real-time IGS correction products for precise point positioning, Presented at the Presentations of the EGU11 Vienna, Austria, April 6, 2011.Google Scholar
Mireault, P.T.Y., Lahaye, F., Collins, P., Caissy, M. and NrCan, . Real-time and near real-time GPS products and services from Canada, Presented at the IGS analysis center workshop 2008, Miami Beach, lorida, USA, June 2–6, 2008.Google Scholar
Elsobeiey, M. and Al-Harbi, S.J.G.S. Performance of real-time Precise Point Positioning using IGS real-time service, GPS Solut, 2016, 20, (3), pp 565571.CrossRefGoogle Scholar
Wang, L., Li, Z., Ge, M., Neitzel, F., Wang, X. and Yuan, H.J.G.S. Investigation of the performance of real-time BDS-only precise point positioning using the IGS real-time service. GPS Solut, May 2019, 23, (3), p 66.CrossRefGoogle Scholar
Eh, S.. Estimation techniques for low-cost inertial navigation, presented at the Report of Geomatics Engineering, University of Calgary, 2005.Google Scholar
Rabbou, M.A. and El-Rabbany, A.J.G.S. Tightly coupled integration of GPS precise point positioning and MEMS-based inertial systems, GPS Solut, 2014, 19, (4), pp 601609.CrossRefGoogle Scholar
Gao, Z., Ge, M., Li, Y., Pan, Y., Chen, Q. and Zhang, H. Modeling of multi-sensor tightly aided BDS triple-frequency precise point positioning and initial assessments, Inform Fusion, March 2020, 55, pp 184198.CrossRefGoogle Scholar
Zhang, Y. and Gao, Y.J.J.O.N. Integration of INS and un-differenced GPS measurements for precise position and attitude determination, J Navigation, 2008, 61, (1), pp 8797.CrossRefGoogle Scholar
Shin, E.H. and Scherzinger, B. Inertially aided precise point positioning, The 22nd International Technical Meeting of Satellite Division of the Institute of Navigation (ION GNSS 2009), 2009, vol. 2001, pp 18921897.Google Scholar
Roesler, G. and Martell, H. Tightly coupled processing of precise point position (PPP) and INS data, Proceedings of ION GPS/GNSS 2009, Institute of Navigation, Savannah, GA, USA, 2009, pp 18981905.Google Scholar
Gao, Z. et al. Tightly coupled integration of multi-GNSS PPP and MEMS inertial measurement unit data, GPS Solut, 2017, 21, (2), pp 377391.CrossRefGoogle Scholar
Niell, A.E. Global mapping functions for the atmosphere delay, J Geophys Res, 1996, 101, (B2), pp 32273246.CrossRefGoogle Scholar
Liu, B., Zhan, X., Liu, M. and Liu, J. GNSS/INS semi-deep integration with federated filtering for high dynamic vehicle, J Aeronaut Astronaut Aviat, 2018, 50, pp 223235.Google Scholar
Groves, P.D. Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, 2nd ed, Artech House, 2013, 776 pp, ISBN-13: 978-1-60807-005-3.Google Scholar
Robert, G.B. and Hwang, P.Y.C. Introduction to Random Signals and Applied Kalman Filtering, 2nd ed, John Wiley, New York, 1992, 512 pp, ISBN 0-47152-573-1.Google Scholar
Kazmierski, K., Sośnica, K. and Hadas, T. Quality assessment of multiGNSS orbits and clocks for realtime precise point positioning, GPS Solut, 2017, 22, (1), p 11.CrossRefGoogle Scholar
Gao, Z. et al. Evaluation on the impact of IMU grades on BDS + GPS PPP/INS tightly coupled integration, Adv Space Res, 2017, 60, (6), pp 12831299.CrossRefGoogle Scholar
Supplementary material: File

Chi et al. supplementary material

Chi et al. supplementary material

Download Chi et al. supplementary material(File)
File 1.4 MB