IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i12p2696-d1170883.html
   My bibliography  Save this article

Carrier Phase Residual Modeling and Fault Monitoring Using Short-Baseline Double Difference and Machine Learning

Author

Listed:
  • Dong-Kyeong Lee

    (Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO 80309, USA)

  • Yebin Lee

    (Department of Aerospace Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea)

  • Byungwoon Park

    (Department of Aerospace Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea)

Abstract

Global Navigation Satellite Systems (GNSS) are used to provide accurate position, navigation, and time (PNT) information to users in various sectors of our society including transportation. Augmentation systems such as differential GNSS (DGNSS), real-time kinematics (RTK), and Precise Point Positioning (PPP) improve the GNSS performance, and providing reliable measurements from its reference station is very crucial. To ensure safe and accurate PNT solutions, code and carrier measurements must be monitored for potential faults or a performance degrade. Although there exist numerous methods to model and monitor the measurements, research on the carrier phase measurements is not as extensive as the code measurements. This paper introduces a split of residuals into receiver noise and multipath components to customize their estimation according to their respective statistical properties. This study also proposes a method to use machine learning-based non-linear regression to effectively model and monitor potential faults in the GNSS measurements including the carrier phase. A training dataset is used to model the nominal quantities of GNSS measurement residuals, and inflation factors are applied to over-bound the fault-free residuals. These inflated residuals are coupled with uncertainty factors to compute thresholds for monitoring carrier phase residuals, and the effectiveness of the thresholds is validated with a test dataset by achieving the false alarm rate of 6.61 × 10 − 6 , slightly lower than the desired level of 10 − 5 .

Suggested Citation

  • Dong-Kyeong Lee & Yebin Lee & Byungwoon Park, 2023. "Carrier Phase Residual Modeling and Fault Monitoring Using Short-Baseline Double Difference and Machine Learning," Mathematics, MDPI, vol. 11(12), pages 1-21, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2696-:d:1170883
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/12/2696/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/12/2696/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wolfgang Niemeier & Dieter Tengen, 2020. "Stochastic Properties of Confidence Ellipsoids after Least Squares Adjustment, Derived from GUM Analysis and Monte Carlo Simulations," Mathematics, MDPI, vol. 8(8), pages 1-18, August.
    2. Yumiao Tian & Maorong Ge & Frank Neitzel, 2020. "Variance Reduction of Sequential Monte Carlo Approach for GNSS Phase Bias Estimation," Mathematics, MDPI, vol. 8(4), pages 1-15, April.
    3. Amin, Md. Tanjin & Khan, Faisal & Imtiaz, Syed, 2018. "Dynamic availability assessment of safety critical systems using a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 108-117.
    4. Yongjun Lee & Byungwoon Park, 2022. "Nonlinear Regression-Based GNSS Multipath Modelling in Deep Urban Area," Mathematics, MDPI, vol. 10(3), pages 1-15, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adumene, Sidum & Khan, Faisal & Adedigba, Sunday & Zendehboudi, Sohrab & Shiri, Hodjat, 2021. "Dynamic risk analysis of marine and offshore systems suffering microbial induced stochastic degradation," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    2. Zhou, Jianxiong & Wei, Shanbi & Chai, Yi, 2021. "Using improved dynamic Bayesian networks in reliability evaluation for flexible test system of aerospace pyromechanical device products," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    3. Huang, Wencheng & Kou, Xingyi & Zhang, Yue & Mi, Rongwei & Yin, Dezhi & Xiao, Wei & Liu, Zhanru, 2021. "Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    4. Xu, Jintao & Gui, Maolei & Ding, Rui & Dai, Tao & Zheng, Mengyan & Men, Xinhong & Meng, Fanpeng & Yu, Tao & Sui, Yang, 2023. "A new approach for dynamic reliability analysis of reactor protection system for HPR1000," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Bhardwaj, U. & Teixeira, A.P. & Guedes Soares, C. & Ariffin, A.K. & Singh, S.S., 2021. "Evidence based risk analysis of fire and explosion accident scenarios in FPSOs," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    6. Tsoumpris, Charalampos & Theotokatos, Gerasimos, 2023. "A decision-making approach for the health-aware energy management of ship hybrid power plants," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Yi Yang & John Dalsgaard Sørensen, 2020. "Probabilistic Availability Analysis for Marine Energy Transfer Subsystem Using Bayesian Network," Energies, MDPI, vol. 13(19), pages 1-27, October.
    8. Guo, Jian & Ma, Kaijiang, 2024. "Risk analysis for hazardous chemical vehicle-bridge transportation system: A dynamic Bayesian network model incorporating vehicle dynamics," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    9. Kai Yu & Lujie Zhou & Pingping Liu & Jing Chen & Dejun Miao & Jiansheng Wang, 2022. "Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management," Mathematics, MDPI, vol. 10(21), pages 1-20, October.
    10. Alsulieman, Abdullah & Ge, Xihe & Zeng, Zhiguo & Butenko, Sergiy & Khan, Faisal & El-Halwagi, Mahmoud, 2024. "Dynamic risk analysis of evolving scenarios in oil and gas separator," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. Haifeng Bian & Jun Zhang & Ruixue Li & Huanhuan Zhao & Xuexue Wang & Yiping Bai, 2021. "Risk analysis of tripping accidents of power grid caused by typical natural hazards based on FTA-BN model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 1771-1795, April.
    12. Taleb-Berrouane, Mohammed & Khan, Faisal & Amyotte, Paul, 2020. "Bayesian Stochastic Petri Nets (BSPN) - A new modelling tool for dynamic safety and reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    13. Zhou, Kaiwen & Xing, Wenbin & Wang, Jingbo & Li, Huanhuan & Yang, Zaili, 2024. "A data-driven risk model for maritime casualty analysis: A global perspective," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    14. Liu, Zengkai & Ma, Qiang & Cai, Baoping & Shi, Xuewei & Zheng, Chao & Liu, Yonghong, 2022. "Risk coupling analysis of subsea blowout accidents based on dynamic Bayesian network and NK model," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    15. Zhao, Xian & Li, Rong & Cao, Shuai & Qiu, Qingan, 2023. "Joint modeling of loading and mission abort policies for systems operating in dynamic environments," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    16. O-Jong Kim & Changdon Kee, 2023. "Wavelet and Neural Network-Based Multipath Detection for Precise Positioning Systems," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    17. Lewis, Austin D. & Groth, Katrina M., 2023. "A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    18. Abaei, Mohammad Mahdi & Hekkenberg, Robert & BahooToroody, Ahmad, 2021. "A multinomial process tree for reliability assessment of machinery in autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    19. Chen, Guohua & Li, Geliang & Xie, Mulin & Xu, Qiming & Zhang, Geng, 2024. "A probabilistic analysis method based on Noisy-OR gate Bayesian network for hydrogen leakage of proton exchange membrane fuel cell," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    20. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Che, Haiyang, 2021. "A Bayesian network for reliability assessment of man-machine phased-mission system considering the phase dependencies of human cognitive error," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2696-:d:1170883. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.