Real-Time Integrity Monitoring for Civil Aviation with Improved Navigation Performance

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 622)


With the worldwide development in the past decades, multi-constellation Global Navigation Satellite System (GNSS) are able to provide consistent and reliable navigation services today, which are expected to bring significant performance improvement to civil aviation in the future. For the GNSS-based aircraft navigation, meeting the integrity and continuity requirements is of the most importance. In the currently proposed baseline Advanced Receiver Autonomous Integrity Monitoring (ARAIM) user algorithm, the integrity risk is evaluated using a conservative upper bound. Despite its computational efficiency, this bound is not tight enough, which may lead to overly conservative results. Operationally, the system may incorrectly alert the user, which severely impacts navigation continuity. Therefore, in this work, we develop a new method to tightly bound the integrity risk and establish a multi-constellation ARAIM test platform to validate the theory. The new approach takes advantage of the independence between position estimation error and detection test statistics and expresses the integrity risk evaluation as a convex optimization problem. It is shown that the global maximum of the objective function is a tight bound on integrity risk, and it can be efficiently computed using an numerical method. Other than the theoretical derivations, another major contribution of this work is prototyping the ARAIM user segment in the Guidance, Navigation, and Control (GNC) laboratory at Shanghai Jiao Tong University. Both of the ARAIM Multiple Hypothesis Solution Separation (MHSS) algorithm and the new approach are incorporated into the prototype, and the real-time integrity monitoring results are visually displayed in terms of horizontal and vertical protection levels, effective monitoring threshold, integrity risk, etc. As compared to the existing MHSS theory, the results suggest that the navigation service availability can be noticeably improved using the proposed method, especially when the constellations are subject to larger ranging errors.


Multi-constellation GNSS Integrity monitoring Integrity risk ARAIM test platform 


  1. 1.
    Mohamed AH, Schwarz KP (1999) Adaptive Kalman filtering for INS/GPS. J Geodesy 73(4):193–203ADSCrossRefGoogle Scholar
  2. 2.
    Alexander I (1993) Perspective/navigation-the global positioning system. In: IEEE spectrum, vol 30, no 12. IEEE pp 36–38Google Scholar
  3. 3.
    Hofmann-Wellenhof B, Lichtenegger H, Wasle E (2007) GNSS-global navigation satellite systems: GPS, GLONASS, Galileo, and more. SpringerGoogle Scholar
  4. 4.
    International Civil Aviation Organization (ICAO), Annex 10:Aeronautical Telecommunications. Volume 1: (Radio Navigation Aids), Amendment 84, published 20 July 2009, effective 19 November 2009, GNSS standards and recommended practices (SARPs) are contained in Section 3.7 and subsections, Appendix B, and Attachment DGoogle Scholar
  5. 5.
    Zhai Y (2018) Ensuring navigation integrity and continuity using multi-constellation GNSS. Ph.D. dissertation, Illinois Institute of Technology, Chicago, ILGoogle Scholar
  6. 6.
    Parkinson B, Axelrad P (1988) Autonomous GPS integrity monitoring using the pseudorange residual. Navigation 35(2):255–274CrossRefGoogle Scholar
  7. 7.
    Patrick Y, Collins R (2006) RAIM-FDE revisited: a new breakthrough in availability performance with nioRAIM (novel integrity-optimized RAIM). In: Proceedings of the 18th international technical meeting of the Satellite Division of the Institute of Navigation (ION GNSS), vol 53, no 1, pp 41-51Google Scholar
  8. 8.
    Ivanov V, Gefan G, Gorbachev O (2011) Global empirical modelling of the total electron content of the ionosphere for satellite radio navigation systems. J Atmos Solar-Terrestrial Phys 73(13):1703–1707ADSCrossRefGoogle Scholar
  9. 9.
    El-Mowafy A, Yang C (2016) Limited sensitivity analysis of ARAIM availability for LPV-200 over Australia using real data. Adv Space Res 57(2):659–670ADSCrossRefGoogle Scholar
  10. 10.
    Li X, Zhang X, Ren X, Fritsche M, Wickert J, Schuh H (2015) Precise positioning with current multi-constellation global navigation satellite systems: GPS, GLONASS, Galileo and BeiDou. Sci Rep 5:8328ADSCrossRefGoogle Scholar
  11. 11.
    Aleshkin AP, Myslivtsev TO, Nikiforov SV, Savochkin PV, Sakhno IV, Semenov AA, Troitskii BV (2019) Calculation of navigation corrections for a single-frequency GNSS receiver based on satellite radio occultation data. Gyroscopy Navig 10(1):15–20CrossRefGoogle Scholar
  12. 12.
    Pan W, Zhan X, Zhang X, Liu B (2019) GNSS/INS integrity monitoring considering nominal bias for civil aircraft CAT-I approach. In: AIAA Scitech 2019 forum, vol 0361Google Scholar
  13. 13.
    Blanch J, Walker T, Enge P, Lee Y, Pervan B, Rippl M, Spletter A, Kropp V (2015) Baseline advanced RAIM user algorithm and possible improvements. IEEE Trans Aerosp Electron Syst 51(1):713–732ADSCrossRefGoogle Scholar
  14. 14.
    Cassel R (2017) Real-time ARAIM using GPS, GLONASS, and Galileo. Master thesis, Illinois Institute of TechnologyGoogle Scholar
  15. 15.
    EU-U.S. Cooperation on Satellite Navigation Working Group C-ARAIM Technical Subgroup (2016) Milestone 3 report. Final versionGoogle Scholar
  16. 16.
    Zhai Y, Joerger M, Pervan B (2015) Continuity and availability in dual-frequency multi-constellation ARAIM. In: Proceedings of the 28th international technical meeting of the Satellite Division of the Institute of Navigation (ION GNSS)Google Scholar
  17. 17.
    Joerger M, Chan F, Pervan B (2014) Solution separation versus residual-based RAIM. NAVIGATION: J Inst Navig 61(4):273–291Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shanghai Jiao Tong UniversityShanghaiChina

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