GPS/INS integration via faded memory Kalman filter

Document Type : Research Paper

Authors

Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran 16846-13114, Iran

10.22075/ijnaa.2024.32800.4876

Abstract

A common technique for navigation and positioning applications is the Global Positioning System (GPS)/Inertial Navigation System (INS) integration, which combines the strengths of GPS and INS to offer accurate and reliable information. As a standalone system, the performance of the INS deteriorates as time is passed. Kalman Filter (KF) is used for GPS/INS integration, and its performance is excellent for simple data. However, in a complex and natural set environment, its performance degrades when the system performs relatively long; therefore, resolving the long-time problem for the GPS/INS system is challenging. The novelty of this paper is GPS/INS integration with the Faded Kalman Filter (FKF). In the FKF, the measurement updates are weighted differently to adapt to changes in the system. This approach allows the filter to adapt to changes or uncertainties in the system dynamics. GPS/INS integration performance is significantly improved using this algorithm rather than a simple KF. An average of 45% reduces the positioning errors compared to traditional KF.

Keywords

[1] E.S. Abdolkarimi, G. Abaei, and M.R. Mosavi, A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications, GPS Solutions 22 (2018), no. 1, 15.
[2] E.S. Abdolkarimi and M.R. Mosavi, Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system, GPS Solutions 24 (2020), no. 2, 35.
[3] E.S. Abdolkarimi and M.R. Mosavi, A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments, GPS Solutions 24 (2020), no. 4, 1–19.
[4] E.S. Abdolkarimi and M.R. Mosavi, A modified neuro-fuzzy system for accuracy improvement of low-cost MEMS-based INS/GPS navigation system, J. Wireless Personal Commun. 129 (2023), 1369–1392.
[5] E.S. Abdolkarimi, M.R. Mosavi, S. Rafatnia, and D. Martin, A hybrid data fusion approach to AI-assisted indirect centralized integrated SINS/GNSS navigation system during GNSS outage, IEEE Access 9 (2021), 100827–100838.
[6] S. Cao, H. Gao, and J. You, In-flight alignment of integrated SINS/GPS/polarization/geomagnetic navigation system based on federal UKF, Sensors 22 (2022), no. 16, 5985.
[7] H. Alaeiyan, M.R. Mosavi, and A. Ayatollahi, Hybrid noise removal to improve the accuracy of inertial sensors using lifting wavelet transform optimized by genetic algorithm, Alexandria Engin. J. 80 (2023), 326–341.
[8] A. Ebrahimi, M. Nezhadshahbodaghi, M.R. Mosavi, and A. Ayatollahi, An improved GPS/INS integration based on EKF and AI during GPS outages, J. Circ. Syst. Comput. 33 (2023), no. 3, 1–23.
[9] M.A. El-Gendy, A.A. Atwan, and A.M. Moussa, An integrated adaptive Kalman filter for improving the reliability of navigation systems, J. Appl. Geodesy 16 (2022), no. 1, 1–14.
[10] M.A. El-Gendy, A.A. Atwan, and A.M. Moussa, GNSS/INS integration based on machine learning LightGBM model for vehicle navigation, Appl. Sci. 12 (2022), no. 11, 5565.
[11] W. Fang, J. Jiang, S. Lu, Y. Gong, Y. Tao, Y. Tang, P. Yan, H. Luo, and J. Liu, A LSTM algorithm estimating pseudo measurements for aiding INS during GNSS signal outages, Remote Sens. 12 (2020), no. 2, 256.
[12] G. Fukuda and N. Kubo, Application of initial bias estimation method for inertial navigation system (INS)/Doppler velocity log (DVL) and INS/DVL/gyrocompass using micro-electro-mechanical system sensors, Sensors 22 (2022), no. 14, 5334.
[13] J. Gante, L. Sousa, and G. Falcao, Dethroning GPS: Low-power accurate 5G positioning systems using machine learning, IEEE J. Emerg. Select. Topics Circ. Syst. 10 (2020), no. 2, 240–252.
[14] C. Hajiyev, U. Hacizade, and D. Cilden-Guler, Integration of barometric and GPS altimeters via adaptive data fusion algorithm, Int. J. Adapt. Control Signal Process. 35 (2021), no. 1, 3–17.
[15] S. Haykin, Kalman Filtering and Neural Networks, John Wiley & Sons, 2004.
[16] H. Jiang, C. Shi, T. Li, Y. Dong, Y. Li, and G. Jing, Low-cost GPS/INS integration with accurate measurement modeling using an extended state observer, GPS Solutions 25 (2021), no. 1, 1–15.
[17] G. Li, Development of cold chain logistics transportation system based on 5G network and Internet of things system, Microproc. Microsyst. 80 (2021), 103565.
[18] D. Li, X. Jia, and J. Zhao, A novel hybrid fusion algorithm for low-cost GPS/INS integrated navigation system during GPS outages, IEEE Access 8 (2020), 53984–53996.
[19] J. Li, J. Wang, Y. Zhang, and S. Wang, Robust variational Bayesian method-based SINS/GPS integrated system, Measurement 163 (2020), 107917.
[20] J. Liu and G. Giu, Vehicle localization during GPS outages with extended Kalman filter and deep learning, IEEE Trans. Instrument. Measur. 70 (2022), 1–10.
[21] A. Noureldin, T.B. Karamat, and J. Georgy, Fundamentals of Inertial Navigation, Satellite-Based Positioning and their Integration, Springer Science & Business Media, 2012.
[22] A.G. Quinchia, G. Falco, E. Falletti, F. Dovis, and C. Ferrer, A comparison between different error modeling of MEMS applied to GPS/INS integrated systems, Sensors 13 (2013), no. 8, 9549–9588.
[23] G. Revach, N. Shlezinger, N. Xiaooyong, A.L. Escoriza, V. Sloun, J.G. Ruud, and C.E. Yonina, KalmanNet: Neural network aided Kalman filtering for partially known dynamics, IEEE Trans. Signal Process. 70 (2022), 1532–1547.
[24] L. Rui, G. Klaus, C. Pengyu, and J. Nan, Collaborative positioning method via GPS/INS and RS/MO multi-source data fusion in multi-target navigation, Survey Rev. 54 (2022), no. 383, 95–105.
[25] D. Simon, Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, John Wiley & Sons, 2006.
[26] S. Wang, J. Li, Y. Zhang, and J. Wang, Deep learning-enabled fusion to bridge GPS outages for INS/GPS integrated navigation, IEEE Sensors J. 22 (2022), no. 9, 8974–8985.
[27] J.L. Weston and D.H. Titterton, Modern inertial navigation technology and its application, Electron. Commun. Engin. J. 12 (2000), no. 2, 49–64.
[28] Y. Zhang, J. Li, J. Wang and S. Wang, Multi-rate strong tracking square-root cubature Kalman filter for MEMSINS/GPS/polarization compass integrated navigation system, Control Engin. Practice 118 (2021), 105184.
[29] S. Zhao, Y. Zhou, and T. Huang, A novel method for AI-assisted INS/GNSS navigation system based on CNNGRU and CKF during GNSS outage, Remote Sens. 14 (2022), no. 18, 4494.
[30] Z. Zhi, D. Liu, and L. Liu, A performance compensation method for GPS/INS integrated navigation system based on CNN–LSTM during GPS outages, Measurement 188 (2022), 516–529.

Articles in Press, Corrected Proof
Available Online from 06 March 2024
  • Receive Date: 10 December 2023
  • Accept Date: 02 February 2024