Advertisement

GPS Solutions

, 23:16 | Cite as

Improved ARAIM fault modes determination scheme based on feedback structure with probability accumulation

  • Qian MengEmail author
  • Jianye Liu
  • Qinghua ZengEmail author
  • Shaojun Feng
  • Rui Xu
Original Article
  • 187 Downloads

Abstract

Advanced receiver autonomous integrity monitoring (ARAIM), based on multi-constellation and dual-frequency, can provide vertical navigation in terminal approaches. The baseline ARAIM receiver algorithm is based on the multiple hypothesis solution separation (MHSS). The fault modes determination scheme, proposed in the ARAIM baseline algorithm, is a sequential structure and a core processing which can be summarized in three steps: calculating the maximum number of simultaneous faults, forming all subsets, and filtering the subsets. The fault modes determined by the maximum number of simultaneous faults are sufficient, but not necessary. A set of redundant fault modes is included, which reduces the ARAIM performance and increases the computation burden. The continuity risk is not considered in the filtering. We propose a new fault modes determination scheme based on feedback structure with probability accumulation. The number of fault modes in the baseline algorithm is defined subject to a given integrity risk requirement. In the proposed algorithm, the fault modes are directly determined subject to this parameter. The fault modes are accumulated in descending order of fault probability. The probability of not monitored risk obtained in probability accumulation is closer to the integrity risk requirement compared to the baseline method, and the number of fault modes is reduced noticeably. The continuity detection is added to the feedback structure to find the severe integrity risk in time. The fault modes, determined in the proposed scheme, are sufficient and necessary. The performance evaluation results under a GPS-Galileo dual-constellation situation for localizer precision vertical 200 (LPV-200) requirements show that the proposed scheme can improve the availability of ARAIM from 84.92 to 92.25%. An enhanced effective monitor threshold (EMT) mainly contributes to this improvement. Furthermore, the proposed scheme also shows positive superiority in calculation load. The reduction in fault modes can directly contribute to the alleviation of the receiver calculation burden, which saves nearly 40% computational time on a PC-based software-defined receiver.

Keywords

ARAIM Fault modes Probability accumulation Feedback structure 

Notes

Acknowledgements

The authors thank Yawei Zhai in Mechanical and Aerospace Engineering at the Illinois Institute of Technology (IIT) for his valuable help. The receiver software is developed based on the open source MATLAB Availability Analysis Simulation Tools (MAAST) developed by the GPS Lab, Stanford University. Thanks for their outstanding work. This research has been funded by the National Natural Science Foundation of China under Grant Nos. 61533008 & 61374115 & 61328301 & 61603181; the Funding of Jiangsu Innovation Program for Graduate Education under Grant No. KYLX16_0379, Fundamental Research Funds for the Central Universities (NS2015037).

References

  1. Blanch J, Walter T, Enge P, Lee Y, Pervan B, Rippl M, Spletter A (2012) Advanced RAIM user algorithm description: Integrity support message processing, fault detection, exclusion, and protection level calculation. In: Proc. ION GNSS 2012, Institute of Navigation, Nashville, September 17–21, pp 2828–2849Google Scholar
  2. Blanch J, Walter T, Enge P (2013) Optimal positioning for advanced RAIM. Navigation 60(4):279–289CrossRefGoogle Scholar
  3. 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):713–732CrossRefGoogle Scholar
  4. Cassel R (2017) Real-time ARAIM Using GPS, GLONASS, and GALILEO. Dissertation, Illinois Institute of TechnologyGoogle Scholar
  5. El-Mowafy A (2017) Advanced receiver autonomous integrity monitoring using triple frequency data with a focus on treatment of biases. Adv Space Res 59(8):2148–2157CrossRefGoogle Scholar
  6. 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):659–670CrossRefGoogle Scholar
  7. Ge Y, Wang Z, Zhu Y (2017) Reduced ARAIM monitoring subset method based on satellites in different orbital planes. GPS Solutions 21(4):1443–1456CrossRefGoogle Scholar
  8. GEAS (2010) GNSS evolutionary architecture study, GEAS phase II panel report. FAA, WashingtonGoogle Scholar
  9. Joerger M, Pervan B (2016) Fault detection and exclusion using solution separation and chi-squared ARAIM. IEEE Trans Aerosp Electron Syst 52(2):726–742CrossRefGoogle Scholar
  10. Khanafseh S, Joerger M, Chan FC, Pervan B (2015) ARAIM integrity support message parameter validation by online ground monitoring. J Navig 68(2):327–337CrossRefGoogle Scholar
  11. Kropp V, Eissfeller B, Berz G (2014) Optimized MHSS ARAIM user algorithms: assumptions, protection level calculation and availability analysis. In: Proc. IEEE/ION PLANS 2014, Institute of Navigation, Monterey, May 5–8, pp 308–323Google Scholar
  12. Lee Y, Bian B (2017) Advanced RAIM performance sensitivity to deviations in ISM parameter values. In: Proc. of ION GNSS + 2017, Institute of Navigation, Portland, September 25–29, pp 2338–2358Google Scholar
  13. Meng Q, Liu JY, Zeng QH, Feng SJ, Chen RZ (2017) Neumann–Hoffman code evasion and stripping method for BeiDou Software-defined receiver. J Navig 70(1):101–119CrossRefGoogle Scholar
  14. Meng Q, Liu JY, Zeng QH, Feng SJ, Xu R (2018) An efficient BeiDou DBZP-based weak signal acquisition scheme for software-defined receiver. IET Radar Sonar Navig 12(6):654–662CrossRefGoogle Scholar
  15. Montenbruck O, Steigenberger P, Hauschild A (2015) Broadcast versus precise ephemerides: a multi-GNSS perspective. GPS Solut 19(2):321–333CrossRefGoogle Scholar
  16. Perea S, Meurer M, Rippl M, Belabbas B, Joerger M (2017) URA/SISA analysis for GPS and Galileo to support ARAIM. Navigation 64(2): 237–254CrossRefGoogle Scholar
  17. Rippl M, Spletter A, Günther C (2011) Parametric performance study of advanced receiver autonomous integrity monitoring (ARAIM) for combined GNSS constellations. In: Proc. ION ITM 2011, Institute of Navigation, San Diego, Jan 24–26, pp 285–295Google Scholar
  18. Walter T, Blanch J, Enge P (2014) Reduced subset analysis for multi-constellation ARAIM. In: Proc. ION ITM 2014, Institute of Navigation, San Diego, Jan 27–29, pp 89–98Google Scholar
  19. Walter T, Blanch J, Joerger M, Pervan B (2016) Determination of fault probabilities for ARAIM. In: Proc. IEEE/ION PLANS 2016, Institute of Navigation, Savannah, April 11–14, pp 451–461Google Scholar
  20. Working Group C (2013) ARAIM Technical Subgroup Milestone 1. Report of the EU-US Cooperation on Satellite Navigation, Working Group C. https://www.gps.gov/policy/cooperation/europe/2013/working-group-c/
  21. Working Group C (2015) ARAIM Technical Subgroup Milestone 2. Report of the EU-US Cooperation on Satellite Navigation, Working Group C. https://www.gps.gov/policy/cooperation/europe/2015/working-group-c/
  22. Working Group C (2016) ARAIM Technical Subgroup Milestone 3. Report of the EU-US Cooperation on Satellite Navigation, Working Group C. https://www.gps.gov/policy/cooperation/europe/2016/working-group-c/
  23. Wu Y, Liu X, Liu W, Ren J, Lou Y, Dai X, Fang X (2017) Long-term behavior and statistical characterization of BeiDou signal-in-space errors. GPS Solut 21(4):1907–1922CrossRefGoogle Scholar
  24. Zhai Y, Joerger M, Pervan B (2015) Continuity and availability in dual-frequency multi-constellation ARAIM. In: Proc. ION GNSS + 2015, Institute of Navigation, Tampa, September 14–18, pp 664–674Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Navigation Research Center, College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Qianxun Spatial Intelligence Inc.ShanghaiChina

Personalised recommendations