Abstract
Aiming at the problem that the traditional fault detection method of tightly coupled GNSS/INS integration is not efficient for small-amplitude faults and gradual faults, a dual-threshold fault detection method based on long-short term memory neural network is proposed. This method establishes GNSS pseudo-range and pseudo-range rate prediction models through a long-short term memory neural network, sets the lower detection threshold based on the accuracy of the prediction model and the residual distribution characteristics, and assists the residual chi-square detection method for fault detection and isolation. The actual measurement data is used for simulation verification from the perspectives of fault detection performance and positioning accuracy. The results show that the proposed method retains the excellent detection performance of the residual detection method for large-amplitude faults, and has better detection sensitivity for small-amplitude faults and gradual faults, which can improve the positioning accuracy and fault detection performance of the system effectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Luo, K.X., Wu, M.P., Fan, Y., et al.: Robust adaptive filtering based on maximum entropy method and its application. Syst. Eng. Electron. 42(3), 667–673 (2020)
Kong, L.T., Wang, W.L., Fan, Y.: Research on algorithm of weighted RAIM for GNSS receiver. J. Telemetry Tracking Command 42(6), 71–77 (2021)
Pan, W.C., Zhan, X.Q., Zhang, X., et al.: A subset-reduced method for FDE ARAIM of tightly-coupled GNSS/INS. Sensors 19(22), 4847 (2019)
Zhang, C., Zhao, X., Pang, C., et al.: The influence of satellite configuration and fault duration time on the performance of fault detection in GNSS/INS integration. Sensors 19(9), 2147 (2019)
Liu, Y.T., Xu, X.S., Liu, X.X., et al.: A fast gradual fault detection method for underwater integrated navigation systems. J. Navig. 69(1), 93–112 (2016)
Zhang, H., Xiao, Y., Yang, C.X.: Integrated navigation system based on fault detection using double state chi-square test. Acta Aeronautica et Astronautica Sinica 41(S2), 724271 (2020)
Liu, S.M., Li, S.H., Zheng, J.T., et al.: Detection of slowly growing faults based on prefilters and two-stage AIME for GNSS/INS ultra-tight integration. Acta Aeronautica et Astronautica Sinica (2020)
Zhong, L.N., Liu, J.Y., Li, R.B., et al.: Approach for detection of slowly growing errors in INS/GNSS tightly-coupled system based on LS-SVM. J. Chin. Inertial Technol. 42(03), 667–673 (2020)
Zhang, C., Zhao, X.B., Pang, C.L., et al.: Joint fault detection method for integrated navigation based on improved AIME-RCTM. In: Proceedings of the 9th China Satellite Navigation Conference (2018)
Kaselimi, M., Voulodimos, A., Doulamis, N., et al.: A causal long short-term memory sequence to sequence model for TEC prediction using GNSS observations. Remote Sens. 12(9), 1354 (2020)
Zhang, G.H., Xu, P.X., Xu, H.S., et al.: Prediction on the urban GNSS measurement uncertainty based on deep learning networks with long short-term memory. IEEE Sens. J. 21(18), 20563–20577 (2021)
Wang, X., Wu, J., Liu, C., et al.: Exploring LSTM based recurrent neural network for failure time series prediction. J. Beijing Univ. Aeronaut. Astronaut. 44(4), 13 (2018)
Li, S.X.: A GPS elevation time series prediction method based on chaos theory and LSTM. J. Navig. Positioning 8(1), 9 (2020)
Han, Z.R., Huang, T.L., Ren, W.J., et al.: Trajectory outlier detection algorithm based on Bi-LSTM model. J. Radars 1(8), 36–43 (2019)
Dong, J.Y., Pang, J.Y., Peng, Y., et al.: Spacecraft telemetry data anomaly detection method based on ensemble LSTM. Chin. J. Sci. Instrum. 40(7), 20–29 (2020)
Chang, Y., Wang, Y., Shen, Y., Ji, C.: A new fuzzy strong tracking cubature Kalman filter for INS/GNSS. GPS Solutions 25(3), 1–15 (2021). https://doi.org/10.1007/s10291-021-01148-5
Zhang, C., Zhao, X.B., Pang, C.L., et al.: Improved fault detection method based on robust estimation and eliding window test for INS/GNSS integration. J. Navig. 73(4), 1–21 (2020)
Zaminpardaz, S., Teunissen, P.J.G.: DIA-datasnooping and identifiability. J. Geodesy 93, 85–101 (2019)
Zhao, X.B., Gao, C., Pang, C.L., et al.: A double-threshold test method for soft faults assisted by BP neural network. Control Decis. Making 35(6), 7 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Aerospace Information Research Institute
About this paper
Cite this paper
Shen, Z., Zhao, X., Pang, C., Zhang, L., Ren, L., Chang, H. (2022). Fault Detection Method of Tightly Coupled GNSS/INS Integration Assisted by LSTM. In: Yang, C., Xie, J. (eds) China Satellite Navigation Conference (CSNC 2022) Proceedings. CSNC 2022. Lecture Notes in Electrical Engineering, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-19-2588-7_49
Download citation
DOI: https://doi.org/10.1007/978-981-19-2588-7_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2587-0
Online ISBN: 978-981-19-2588-7
eBook Packages: EngineeringEngineering (R0)