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A novel hybrid observation prediction methodology for bridging GNSS outages in INS/GNSS systems

Published online by Cambridge University Press:  23 May 2022

Linzhouting Chen Open the ORCID record for Linzhouting Chen [Opens in a new window] ,
Zhanchao Liu Open the ORCID record for Zhanchao Liu [Opens in a new window]  and
Jiancheng Fang
Linzhouting Chen*
Affiliation:
College of Aerospace Engineering, Guizhou Institute of Technology, Guiyang, China
Zhanchao Liu
Affiliation:
Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China
Jiancheng Fang
Affiliation:
Key Laboratory of Fundamental Science for National Defense-Novel Inertial Instrument and Navigation System Technology, Beihang University, Beijing, China
*
*Corresponding author. E-mail: chenlzt@163.com
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Abstract

The integration of the inertial navigation system (INS) and global navigation satellite system (GNSS) is suited for localisation and navigation applications, such as aircrafts, land vehicles and ships. The primary challenge is for navigation system to achieve accurate and reliable navigation solution during GNSS outages. This paper presents an observation prediction methodology for INS/GNSS bridging GNSS outages, which combines partial least squares regression (PLSR) and Gaussian process regression (GPR) to model the INS/GNSS observations and enable a Kalman filter to estimate INS errors. The performance of proposed PLSR/GPR prediction methodology was validated through four GNSS outages taken on flight experiment data, including diverse manoeuvre conditions. The experiment results demonstrate that remarkable performance enhancements are achieved through applying the proposed PLSR/GPR prediction methodology into INS/GNSS integration.

Keywords

INS/GNSSGNSS outagesobservation predictionhybrid PLSR/GPR modelHP filter

Information

Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 5 , September 2022 , pp. 1206 - 1225
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Adusumilli, S., Bhatt, D., Wang, H., Devabhaktuni, V. and Bhattacharya, P. (2015). A novel hybrid approach utilizing principal component regression and random forest regression to bridge the period of GPS outages. Neurocomputing, 166, 185–192.CrossRefGoogle Scholar
Areekul, P., Senjyu, T., Toyama, H. and Yona, A. (2010). A hybrid ARIMA and neural network model for short-term price forecasting in deregulated market. IEEE Transactions on Power Systems, 25(3), 524–530.CrossRefGoogle Scholar
Atia, M. M., Noureldin, A. and Korenberg, M. (2011). Gaussian process regression approach for bridging GPS outages in integrated navigation systems. Electronics Letters, 47(1), 52–53.CrossRefGoogle Scholar
Belhajem, I., Maissa, Y. B. and Tamtaoui, A. (2018). Improving vehicle localization in a smart city with low cost sensor networks and support vector machines. Mobile Networks and Applications, 23(4), 854–863.CrossRefGoogle Scholar
Bhatt, D., Aggarwal, P., Devabhaktuni, V. and Bhattacharya, P. (2014). A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS. Expert Systems with Applications, 41(5), 2166–2173.CrossRefGoogle Scholar
Chen, L. Z. T. and Fang, J. C. (2014). A hybrid prediction method for bridging GPS outages in high-precision POS application. IEEE Transaction on Instrumentation and Measurement, 63(6), 1656–1665.10.1109/TIM.2013.2292277CrossRefGoogle Scholar
Chen, X., Shen, C., Zhang, W. B., Tomizuka, M., Xu, Y. and Chiu, K. (2013). Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages. Measurement, 46(10), 3847–3854.CrossRefGoogle Scholar
Chen, H., Cheng, X., Wang, H. and Han, X. (2014). Dealing with observation outages within navigation data using Gaussian process regression. The Journal of Navigation, 67(4), 603–615.CrossRefGoogle Scholar
Chiang, K. W., Huang, Y. W., Li, C. Y. and Chang, H. W. (2011). An ANN embedded RTS smoother for an INS/GPS integrated Positioning and Orientation System. Applied Soft Computing, 11(21), 2633–2644.CrossRefGoogle Scholar
Ding, C., Duan, J. X., Zhang, Y. R., Wu, X. K. and Yu, G. Z. (2018). Using an ARIMA-GARCH modeling approach to improve subway short-term ridership forecasting accounting for dynamic volatility. IEEE Transactions on Intelligent Transportation Systems.IEEE Transactions on Intelligent Transportation Systems, 19(4), 1054–1064.CrossRefGoogle Scholar
Einicke, G. A., Falco, G. and Malos, J. T. (2013). Bounded constrained filtering for GPSINS integration. IEEE Transactions on Automatic Control, 58(1), 125–133.10.1109/TAC.2012.2223362CrossRefGoogle Scholar
Gan, R. J., Tan, J. Y., Mo, L. Y., Li, Y. and Huang, D. Z. (2020). Using partial least squares regression to fit small data of H7N9 incidence based on the baidu index. IEEE Access, 8, 60392–60400.CrossRefGoogle Scholar
Godha, S. and Cannon, M. E. (2007). GPS/MEMS INS integrated system for navigation in urban areas. GPS Solutions, 11(3), 193–203.CrossRefGoogle Scholar
Groves, P. D. (2013). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems (2nd ed.). Norwood, MA, USA: Artech House.Google Scholar
Hasan, A. M., Samsudin, K., Ramli, A. R. and Azmir, R. S. (2011a). Automatic estimation of inertial navigation system errors for global positioning system outage recovery. International Journal of Aerospace Engineering, 225(1), 86–96.Google Scholar
Hasan, A. M., Samsudin, K. and Ramli, A. R. (2011b). Optimizing of ANFIS for estimating INS error during GPS outages. Journal of the Chinese Institute of Engineers, 34(7), 967–982.CrossRefGoogle Scholar
Li, X., Chen, W., Chan, C. Y., Li, B. and Song, X. H. (2019). Multi-sensor fusion methodology for enhanced land vehicle positioning. Information Fusion, 46, 51–62.CrossRefGoogle Scholar
Maravall, A. G. and Rio, A. D. (2001). Time aggregation and the hodrick–prescott filter. Documentos de Trabajo/Banco de Espana, 1, 1–43.Google Scholar
Noureldin, A., Karamat, T. B., Eberts, M. D. and El-Shafie, A. (2009). Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications. IEEE Transactions on Vehicular Technology, 58(3), 1077–1096.CrossRefGoogle Scholar
Ren, X. P., Wang, J., Xue, Z. C. and Gu, M. Q. (2013). Performance assisted enhancement based on change point detection and Kalman filtering. Journal of Central South University, 20(12), 3528–3535.CrossRefGoogle Scholar
Semeniuk, L. and Noureldin, A. (2006). Bridging GPS outages using neural network estimates of INS position and velocity errors. Measurement Science & Technology, 17(10), 2783–2798.CrossRefGoogle Scholar
Sharaf, R. and Noureldin, A. (2007). Sensor integration for satellite-based vehicular navigation using neural networks. IEEE Transactions on Neural Networks, 18(2), 589–594.CrossRefGoogle ScholarPubMed
Sharaf, R., Noureldin, A., Osman, A. and El-Sheimy, N. (2005). Online INS/GPS integration with a radial basis function neural network. IEEE Aerospace and Electronic Systems Magazine, 20(3), 8–14.CrossRefGoogle Scholar
Shen, C., Zhang, Y., Tang, J., Cao, H. L. and Liu, J. (2019). Dual-optimization for a MEMS-INS/GPS system during GPS outages based on the cubature kalman filter and neural networks. Mechanical Systems and Signal Processing, 133, 1–14.CrossRefGoogle Scholar
Shi, B., Lu, X. S. and Chen, Y. F. (2012). Improving position and attitude precision of GPS/INS by applying EKF smoothing algorithmm. Journal of Geoscience and Technology, 29(5), 334–338.Google Scholar
Song, K. S., Li, L., Li, S. and Tedesco, L. (2014). Using partial least squares-artificial neural network for inversion of inland water chlorophyll-a. IEEE Transactions on Geoscience and Remote Sensing, 52(2), 1502–1517.CrossRefGoogle Scholar
Tan, X., Wang, J., Jin, S. and Meng, X. (2015). GA-SVR and Pseudo-position-aided GPS/INS integration during GPS outage. The Journal of Navigation, 68(4), 678–696.CrossRefGoogle Scholar
Xu, Q., Li, X. and Chan, C. Y. (2018). Enhancing localization accuracy of MEMS-INS/GPS/In-vehicle sensors integration during GPS outages. IEEE Transaction on Instrumentation and Measurement, 67(8), 1966–1978.CrossRefGoogle Scholar
Yang, Y. X. and Xu, T. H. (2003). An adaptive kalman filter based on sage windowing weights and variance components. The Journal of Navigation, 56(2), 231–240.CrossRefGoogle Scholar
Yao, Y. Q. and Xu, X. S. (2017). A RLS-SVM aided fusion methodology for INS during GPS outages. Sensors, 17(3), 432–444.CrossRefGoogle ScholarPubMed
Ye, W., Li, J. L., Fang, J. C. and Yuan, X. Z. (2018a). EGP-CDKF for performance improvement of the SINS/GNSS integrated system. IEEE Transactions on Industrial Electronics, 65(4), 3601–3609.CrossRefGoogle Scholar
Ye, W., Liu, Z. C., Li, C. and Fang, J. C. (2018b). Enhanced kalman filter using noisy input Gaussian process regression for bridging GPS outages in a POS. The Journal of Navigation, 71(3), 565–584.CrossRefGoogle Scholar
Ye, W., Wang, B., Liu, Y. H., Gu, B. and Chen, H. M. (2020). Deep Gaussian process regression for performance improvement of POS during GPS outages. IEEE Access, 8, 117483–117492.CrossRefGoogle Scholar
Zihajehzadeh, S. and Park, E. J. (2017). A Gaussian process regression model for walking speed estimation using a head-worn IMU. Proc. IEEE EMBC Engineering in Medicine and Biology Society, 2345–2348.CrossRefGoogle Scholar