Hostname: page-component-cb9f654ff-qc88w Total loading time: 0 Render date: 2025-08-13T08:50:19.381Z Has data issue: false hasContentIssue false
  • English
  • Français

Multiple and mixed abnormal measurement processing in multi-GNSS positioning and navigation based on the resilient a priori innovation and posterior residual for harsh environments

Published online by Cambridge University Press:  11 August 2025

Bohan Jin ,
Zhetao Zhang Open the ORCID record for Zhetao Zhang [Opens in a new window] ,
Haijun Yuan  and
Xuezhen Li
Bohan Jin
Affiliation:
Hubei Luojia Laboratory, Wuhan, China State Key Laboratory of Spatial Datum, Xi’an, China School of Earth Sciences and Engineering, Hohai University, Nanjing, China
Zhetao Zhang*
Affiliation:
Hubei Luojia Laboratory, Wuhan, China State Key Laboratory of Spatial Datum, Xi’an, China School of Earth Sciences and Engineering, Hohai University, Nanjing, China
Haijun Yuan
Affiliation:
Hubei Luojia Laboratory, Wuhan, China State Key Laboratory of Spatial Datum, Xi’an, China School of Earth Sciences and Engineering, Hohai University, Nanjing, China
Xuezhen Li
Affiliation:
Hubei Luojia Laboratory, Wuhan, China State Key Laboratory of Spatial Datum, Xi’an, China School of Earth Sciences and Engineering, Hohai University, Nanjing, China
*
Corresponding author: Zhetao Zhang; Email: zt.zhang@hotmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Global Navigation Satellite System (GNSS) positioning accuracy is challenged due to abnormal signals in harsh environments. This study proposes an approach for multiple and mixed abnormal measurement processing in multi-GNSS positioning and navigation based on the resilient a priori innovation and posterior residual (PR) for harsh environments. Specifically, first, both static and kinematic processing modes are considered when calculating the innovation vector (IV). Second, observations are classified and abnormal measurements are eliminated based on the different observation accuracies of different GNSS systems within the resilient IV method. Finally, the resilient PR method considers the total number of redundant observations. Compared with the traditional IV and PR method, the RIP method improves the positioning accuracy by approximately 30.2% and 58.0% in static experimental datasets No. 1 and No. 2, respectively. In the kinematic experiment, it improves the ambiguity success rate and positioning accuracy by approximately 41.5% and 86.7%, respectively.

Keywords

GNSSfault detectionmultipatha priori innovationa posterior residual

Information

Type
Research Article
Information
The Journal of Navigation , First View , pp. 1 - 20
Check for updates
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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.)

Article purchase

Temporarily unavailable

References

Ashkenazi, V., Moore, T. and Westrop, J. (1991). Combining Pseudo-Range and Phase for Dynamic GPS. Proceedings of the Kinematic Systems in Geodesy, Surveying, and Remote Sensing: Symposium No. 107 Banff, Alberta, Canada, September 10–13, 1990. https://doi.org/10.1007/978-1-4612-3102-8_30.Google Scholar
Baarda, W. (1968). A Testing Procedure for Use in Geodetic Networks. Proceedings of Publications on Geodesy.10.54419/t8w4sgCrossRefGoogle Scholar
Crespillo, O., Grosch, A. and Meurer, M. (2017). Detection of DME Ranging Faults with INS Coupling. Proceedings of the 2017 Integrated Communications, Navigation and Surveillance Conference (ICNS). https://doi.org/10.1109/ICNSURV.2017.8011923.Google Scholar
Dach, R., Schaer, S., Lutz, S., Meindl, M. and Beutler, G. (2010). Combining the Observations from Different GNSS. Proceedings of the EUREF 2010 Symposium, Gävle, Sweden.Google Scholar
Grosch, A., Crespillo, O., Martini, I. and Günther, C. (2017). Snapshot Residual and Kalman Filter Based Fault Detection and Exclusion Schemes for Robust Railway Navigation. Proceedings of the 2017 European Navigation Conference (ENC). https://doi.org/10.1109/EURONAV.2017.7954171.Google Scholar
Hofmann-Wellenhof, B., Lichtenegger, H. and Wasle, E. (2007). GNSS–Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and More. Springer Science & Business Media.Google Scholar
Hsu, L. (2018). Analysis and Modeling GPS NLOS Effect in Highly Urbanized Area. GPS Solutions, 22, 7. https://doi.org/10.1007/s10291-017-0667-9.CrossRefGoogle Scholar
Huber, P. (1992). Robust Estimation of a Location Parameter. Springer. https://doi.org/10.1007/978-1-4612-4380-9_35.CrossRefGoogle Scholar
Jiang, W., Chen, Y., Chen, Q., Chen, H., Pan, Y., Liu, X. and Liu, T. (2022). High Precision Deformation Monitoring with Integrated GNSS and Ground Range Observations in Harsh Environment. Measurement, 204, 112179. https://doi.org/10.1016/j.measurement.2022.112179.CrossRefGoogle Scholar
Jin, B., and Zhang, Z. (2025). Analysis and Improvement of GNSS Precise Kinematic Positioning Performance in Several Typical Occlusion Situations. Navigation Positioning and Timing, 12(01), 60–75. https://doi.org/10.19306/j.cnki.2095-8110.2025.01.006.Google Scholar
Koch, K. (2015). Minimal Detectable Outliers as Measures of Reliability. Journal of Geodesy, 89, 483–490. https://doi.org/10.1007/s00190-015-0793-5.CrossRefGoogle Scholar
Lehmann, R. (2012). Improved Critical Values for Extreme Normalized and Studentized Residuals in Gauss–Markov Models. Journal of Geodesy, 86, 1137–1146. https://doi.org/10.1007/s00190-012-0569-0.CrossRefGoogle Scholar
Lehmann, R. (2013). On the Formulation of the Alternative Hypothesis for Geodetic Outlier Detection. Journal of Geodesy, 87, 373–386. https://doi.org/10.1007/s00190-012-0607-y.CrossRefGoogle Scholar
Leick, A. and Emmons, M. (1994). Quality control with reliability for large GPS networks. Journal of Surveying Engineering, 120, 25–41. https://doi.org/10.1061/(ASCE)0733-9453(1994)120:1(25).CrossRefGoogle Scholar
Leick, A., Rapoport, L. and Tatarnikov, D. (2015). GPS Satellite Surveying. John Wiley & Sons.10.1002/9781119018612CrossRefGoogle Scholar
Li, B., Shen, Y., Feng, Y., Gao, W. and Yang, L. (2014). GNSS Ambiguity Resolution with Controllable Failure Rate for Long Baseline Network RTK. Journal of Geodesy, 88, 99–112. https://doi.org/10.1007/s00190-013-0670-z.CrossRefGoogle Scholar
Li, X., Zhang, Z., Li, Y., Luo, X. and Ferreira, V. (2023). A Priori and Effective Estimation of Variance Factors Based on the Code Chipping Rate in BeiDou Navigation Satellite System Positioning. Studia Geophysica et Geodaetica, 67, 39–59. https://doi.org/10.1007/s11200-022-0452-2.CrossRefGoogle Scholar
Li, Y., Zhang, Z., He, X., Yuan, H. and Zang, N. (2022). An Elevation Stochastic Model Constrained by C/N0 for GNSS Real-Time Kinematic Positioning in Harsh Environments. Measurement Science and Technology, 34, 015011. https://doi.org/10.1088/1361-6501/ac900d.CrossRefGoogle Scholar
Mehra, R. and Peschon, J. (1971). An Innovations Approach to Fault Detection and Diagnosis in Dynamic Systems. Automatica, 7, 637–640. https://doi.org/10.1016/0005-1098(71)90028-8.CrossRefGoogle Scholar
Paziewski, J. (2020). Recent Advances and Perspectives for Positioning and Applications with Smartphone GNSS Observations. Measurement Science and Technology, 31, 091001. https://doi.org/10.1088/1361-6501/ab8a7d.CrossRefGoogle Scholar
Sun, R., Zhang, Z., Cheng, Q. and Ochieng, W. (2022). Pseudorange Error Prediction for Adaptive Tightly Coupled GNSS/IMU Navigation in Urban Areas. GPS Solutions, 26, 1–13. https://doi.org/10.1007/s10291-021-01213-z.CrossRefGoogle Scholar
Takasu, T. (2009). RTKLIB: Open Source Program Package for RTK-GPS. Proceedings of the FOSS4G.Google Scholar
Teunissen, P. (1997). A Canonical Theory for Short GPS Baselines. Part IV: Precision versus Reliability. Journal of Geodesy, 71, 513–525. https://doi.org/10.1007/s001900050119.CrossRefGoogle Scholar
Teunissen, P. (2006). Testing Theory. VSSD Delft.Google Scholar
Teunissen, P. (2018). Distributional Theory for the DIA Method. Journal of Geodesy, 92, 59–80. https://doi.org/10.1007/s00190-017-1045-7.CrossRefGoogle Scholar
Teunissen, P. and Montenbruck, O. (2017). Springer Handbook of Global Navigation Satellite Systems. Springer.10.1007/978-3-319-42928-1CrossRefGoogle Scholar
Wen, W., Zhang, G. and Hsu, L. (2021). GNSS Outlier Mitigation via Graduated Non-Convexity Factor Graph Optimization. IEEE Transactions on Vehicular Technology, 71, 297–310. https://doi.org/10.1109/TVT.2021.3130909.CrossRefGoogle Scholar
Won, D., Ahn, J., Lee, E., Heo, M., Sung, S. and Lee, Y. (2015). GNSS Carrier Phase Anomaly Detection and Validation for Precise Land Vehicle Positioning. IEEE Transactions on Instrumentation and Measurement, 64, 2389–2398. https://doi.org/10.1109/TIM.2015.2415091.CrossRefGoogle Scholar
Yang, L., Shen, Y., Li, B. and Rizos, C. (2021). Simplified Algebraic Estimation for the Quality Control of DIA Estimator. Journal of Geodesy, 95, 1–15. https://doi.org/10.1007/s00190-020-01454-9.CrossRefGoogle Scholar
Yang, Y. (2006). Adaptive Navigation and Kinematic Positioning. Surveying and Mapping Press.Google Scholar
Yang, Y., He, H. and Xu, T. (2001). Adaptive Robust Filtering for Kinematic GPS Positioning. ACTA GEODAETICA et CARTOGRAPHICA SINICA, 30, 293–298.Google Scholar
Yuan, H., He, X., Zhang, Z., Liu, H., Li, Y. and Jiang, Z. (2023). A Real-Time Combined Quality Control Method for GNSS Precise Positioning in Harsh Environments. Advances in Space Research, 71, 900–911. https://doi.org/10.1016/j.asr.2022.08.026.CrossRefGoogle Scholar
Yuan, H., Zhang, Z., He, X., Wen, Y. and Zeng, J. (2022). An Extended Robust Estimation Method Considering the Multipath Effects in GNSS Real-Time Kinematic Positioning. IEEE Transactions on Instrumentation and Measurement, 71, 1–9. https://doi.org/10.1109/TIM.2022.3193967.Google Scholar
Zabalegui, P., De Miguel, G., Mendizabal, J. and Adin, I. (2022). Innovation-Based Fault Detection and Exclusion Applied to Ultra-WideBand Augmented Urban GNSS Navigation. Remote Sensing, 15, 99. https://doi.org/10.3390/rs15010099.CrossRefGoogle Scholar
Zhang, Z., Li, B., Shen, Y. and Yang, L. (2017). A Noise Analysis Method for GNSS Signals of a Standalone Receiver. Acta Geodaetica et Geophysica, 52, 301–316. https://doi.org/10.1007/s40328-016-0189-x.CrossRefGoogle Scholar
Zhang, Z., Li, X. and Yuan, H. (2023a). Best Integer Equivariant Estimation based on Unsupervised Machine Learning for GNSS Precise Positioning and Navigation in Complex Environments. IEEE Transactions on Aerospace and Electronic Systems, 60, 2672–2682. https://doi.org/10.1109/TAES.2023.3320115.CrossRefGoogle Scholar
Zhang, Z., Li, X., Yuan, H. and Luo, Y. (2023b). An Enhanced Outlier Processing Approach based on the Resilient Mathematical Model Compensation in GNSS Precise Positioning and Navigation. Measurement Science and Technology, 35, 015007. https://doi.org/10.1088/1361-6501/acfc5c.CrossRefGoogle Scholar
Zhang, Z., Li, Y., He, X., Chen, W. and Li, B. (2022). A Composite Stochastic Model Considering the Terrain Topography for Real-Time GNSS Monitoring in Canyon Environments. Journal of Geodesy, 96, 79. https://doi.org/10.1007/s00190-022-01660-7.CrossRefGoogle Scholar
Zhang, Z., Li, Y., He, X. and Hsu, L. (2023c). Resilient GNSS Real-Time Kinematic Precise Positioning with Inequality and Equality Constraints. GPS Solutions, 27, 116. https://doi.org/10.1007/s10291-023-01454-0.CrossRefGoogle Scholar
Zhang, Z. and Wang, H. (2025). Integrated BeiDou Satellite-Based Augmentation System Framework Combining B2a and B1C Services. GPS Solutions, 29, 74. https://doi.org/10.1007/s10291-025-01834-8.CrossRefGoogle Scholar
Zhang, Z., Wang, L. and Li, X. (2025). Characterization and Modeling of GNSS Site-Specific Unmodeled Errors under Reflection and Diffraction Using a Data-Driven Approach. Satellite Navigation, 6, 8. https://doi.org/10.1186/s43020-025-00161-0.CrossRefGoogle Scholar
Zhang, Z., Yuan, H., He, X., Chen, B. and Zhang, Z. (2023d). Unmodeled-Error-Corrected Stochastic Assessment for a Standalone GNSS Receiver Regardless of the Number of Tracked Frequencies. Measurement, 206, 112265. https://doi.org/10.1016/j.measurement.2022.112265.CrossRefGoogle Scholar
Zhang, Z., Yuan, H., He, X., Li, B. and Geng, J. (2023e). Best Integer Equivariant Estimation With Quality Control in GNSS RTK for Canyon Environments. IEEE Transactions on Aerospace and Electronic Systems, 59, 4105–4117. https://doi.org/10.1109/TAES.2023.3236916.CrossRefGoogle Scholar