Optimization of SINS / GNSS Integrated Navigation Model Based on Prior Information
DOI:
https://doi.org/10.54097/ykb61917Keywords:
SINS, GNSS, loose combination, prior information.Abstract
Inertial navigation systems (INS) are extensively utilized across various domains due to their independence from external information during operation, commendable autonomy and stealth, and capability to deliver comprehensive data output. The loose combination navigation of SINS/GNSS benefits from the complementing function of the Global Navigation Satellite System (GNSS), characterized by its straightforward principle and ease of implementation through the integration of GNSS data with the auxiliary correction provided by INS data. This work addresses the error issue in inertial navigation by proposing a filtering method utilizing a second-order Butterworth low-pass filter to extract a priori information from the inertial navigation data. Subsequently, it examines the impact of including a priori information on the efficacy of the SINS/GNSS loose-combination navigation. The findings indicate that incorporating a priori information can effectively rectify navigation trajectory deviations, enhance the convergence rate of misalignment angle errors, expedite the convergence of three-dimensional velocity errors in the northeastern sky, and diminish the amplitude of position error fluctuations. The precision of SINS/GNSS loose combination navigation is markedly enhanced.
Downloads
References
[1] Borodacz, K., Szczepański, C., & Popowski, S. (2021). Review and selection of commercially available IMU for a short time inertial navigation. Aircraft Engineering and Aerospace Technology. DOI: https://doi.org/10.1108/AEAT-12-2020-0308
[2] Lyu P., Wang B., Lai J., Bai S., Liu M., Yu W. (2023), A factor graph optimization method for high-precision IMU-based navigation system, IEEE Transactions on Instrumentation and Measurement, Vol. 72, pp. 1-12.
[3] Mahdi A. E., Azouz A., Abdalla A. E., Abosekeen A. (2022), IMU-Error Estimation and Cancellation Using ANFIS for Improved UAV Navigation, Proceedings of the 2022 13th International Conference on Electrical Engineering (ICEENG), pp. 120-124. DOI: https://doi.org/10.1109/ICEENG49683.2022.9782058
[4] Sun M., Wang Y., Joseph W., Plets D. (2022), Indoor Localization Using Mind Evolutionary Algorithm-Based Geomagnetic Positioning and Smartphone IMU Sensors, IEEE Sensors Journal, Vol. 22, No. 7, pp. 7130-7141. DOI: https://doi.org/10.1109/JSEN.2022.3155817
[5] Xu R., Chen S., Bai S., Wen W. (2024), Nonlinearity-Aware ZUPT-Aided Pedestrian Inertial Navigation Based on Cubature Kalman Filter in Urban Canyons, IEEE Transactions on Instrumentation and Measurement, Vol. 73, pp. 1-15. DOI: https://doi.org/10.1109/TIM.2024.3451578
[6] Ibrahim A., Abosekeen A., Azouz A., Noureldin A. (2023), Enhanced Autonomous Vehicle Positioning Using a Loosely Coupled INS/GNSS-Based Invariant-EKF Integration, Sensors, Vol. 23, No. 13, Article No. 6097. DOI: https://doi.org/10.3390/s23136097
[7] Zhu F., Cai Q., Tao X., Zhang X., Liu W. (2024), POSMind: developing a hierarchical GNSS/SINS post-processing service system for precise position and attitude determination, GPS Solut., Vol. 28, No. 3, pp. 1-21. DOI: https://doi.org/10.1007/s10291-024-01683-x
[8] Chen Q., Zhang Q., Niu X., Liu J. N. (2021), Semi-analytical assessment of the relative accuracy of the GNSS/INS in railway track irregularity measurements, Satellite Navigation, Vol. 2, pp. 1-16. DOI: https://doi.org/10.1186/s43020-021-00057-9
[9] Yao Y., Xu X., Zhu C., Chan C.-Y. (2017), A hybrid fusion algorithm for GPS/INS integration during GPS outages, Measurement, Vol. 103, pp. 42-51. DOI: https://doi.org/10.1016/j.measurement.2017.01.053
[10] Mahdi A. E., Azouz A., Abdalla A. E., Abosekeen A. (2022), A Machine Learning Approach for an Improved Inertial Navigation System Solution, Sensors, Vol. 22, No. 4, Article No. 1687. DOI: https://doi.org/10.3390/s22041687
[11] Damagatla R. K., Atia M. (2024), Improving EKF-Based IMU/GNSS Fusion Using Machine Learning for IMU Denoising, IEEE Access, Vol. 12, pp. 114358-114369. DOI: https://doi.org/10.1109/ACCESS.2024.3440314
[12] Liu J., Guo G. (2021), Vehicle Localization During GPS Outages With Extended Kalman Filter and Deep Learning, IEEE Transactions on Instrumentation and Measurement, Vol. 70, pp. 1-10. DOI: https://doi.org/10.1109/TIM.2021.3097401
[13] Iyer K., Dey A., Xu B., Sharma N., Hsu L.-T. (2024), Enhancing Positioning in GNSS Denied Environments Based on an Extended Kalman Filter Using Past GNSS Measurements and IMU, IEEE Transactions on Vehicular Technology, Vol. 73, No. 6, pp. 7908-7924. DOI: https://doi.org/10.1109/TVT.2024.3360076
[14] Groves P. (2007), Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems.
[15] Chang L., Luo Y. (2023), Log-Linear Error State Model Derivation Without Approximation for INS, IEEE Transactions on Aerospace and Electronic Systems, Vol. 59, No. 2, pp. 2029-2035.
[16] Luo Y., Lu F., Guo C., Liu J. (2024), Matrix Lie Group-Based Extended Kalman Filtering for Inertial-Integrated Navigation in the Navigation Frame, IEEE Transactions on Instrumentation and Measurement, Vol. 73, pp. 1-16. DOI: https://doi.org/10.1109/TIM.2023.3329103
[17] Wang J., Chen W., Weng D. (2025), GNSS/IMU/map-matching feedback integration with adaptive GNSS accuracy estimation by using low-quality sensors for vehicle localization in urban canyon, Measurement, Vol. 240, pp. 115541. DOI: https://doi.org/10.1016/j.measurement.2024.115541
[18] Nassar S., El-Sheimy N. (2006), A Combined Algorithm of Improving INS Error Modeling and Sensor Measurements for Accurate INS/GPS Navigation, GPS Solutions, Vol. 10, pp. 29-39. DOI: https://doi.org/10.1007/s10291-005-0149-3
[19] Lyu P., Wang B., Lai J., Bai S., Liu M., Yu W. (2023), A Factor Graph Optimization Method for High-Precision IMU-Based Navigation System, IEEE Transactions on Instrumentation and Measurement, Vol. 72, pp. 1-12. DOI: https://doi.org/10.1109/TIM.2023.3291779
[20] Tang J., Bian H., Ma H., Wang R. (2024), SINS/GNSS Integrated Navigation Based on Invariant Error Models in Inertial Frame, IEEE Sensors Journal, Vol. DOI: https://doi.org/10.1109/JSEN.2023.3346873
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Academic Journal of Science and Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.








