Skip to main content
SpringerLink
Log in

INS/magnetometer integrated positioning based on neural network for bridging long-time GPS outages

  • Original Article
  • Published:
GPS Solutions Aims and scope Submit manuscript

Abstract

In global position system (GPS) and inertial navigation system (INS) integrated navigation systems, the positioning method based on artificial intelligence (AI) learning algorithms has the disadvantage of position diverging as the GPS outages time continues. To solve this problem, INS/magnetometer integrated positioning based on neural network is proposed for bridging GPS outages over a long time. First, the possibility of using magnetometer for positioning is verified by analyzing the international geomagnetic reference field model and the magnetometer measurement model. Then, a magnetometer-assisted positioning solution is proposed. This solution includes four parts: the magnetic fields update module with adaptive extended Kalman filter (AEKF), predictor by AI, INS/GPS integration with KF, and INS/position integration with AEKF. The simulation and driving test results show that the proposed method can keep most of the position errors within a certain range and there is no tendency of divergence at all.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  • Abdolkarimi ES, Abaei G, Mosavi MR (2018) A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications. GPS Solut 22(1):1–13

    Article  Google Scholar 

  • Adusumilli S, Bhatt D, Wang H, Bhattacharya P, Devabhaktuni V (2013) A low-cost INS/GPS integration methodology based on random forest regression. Expert Syst Appl 40(11):4653–4659

    Article  Google Scholar 

  • Adusumilli S, Bhatt D, Wang H, Devabhaktuni V, 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

    Article  Google Scholar 

  • Fang J, Yang S (2011) Study on innovation adaptive EKF for in-flight alignment of airborne POS. IEEE Trans Instrum Meas 60(4):1378–1388

    Article  Google Scholar 

  • Noureldin A, El-Shafie A, Bayoumi M (2011) GPS/INS integration utilizing dynamic neural networks for vehicular navigation. Inf Fusion 12(1):48–57

    Article  Google Scholar 

  • Semeniuk L, Noureldin A (2006) Bridging GPS outages using neural network estimates of INS position and velocity errors. Meas Sci Technol 17(10):2783–2798

    Article  Google Scholar 

  • Thébault E et al (2010) International geomagnetic reference field: the eleventh generation. Geophys J Int 183(3):1216–1230

    Article  Google Scholar 

  • Wang W, Wu Z, Zhang H (2018) An adaptive cascaded Kalman filter for two-antenna GPS/MEMS-IMU integration. In: IEEE/ION PLANS 2018, Institute of Navigation, Monterey, CA, USA, 23–26 April, pp 833–837

  • Wu Y, Pei L (2017) Gyroscope calibration via magnetometer. IEEE Sens J 17(16):5269–5275

    Article  Google Scholar 

  • Wu Z, Wang W (2018) Magnetometer and gyroscope calibration method with level rotation. Sensors 18(3):748

    Article  Google Scholar 

  • Wu Y, Zou D, Liu P, Yu W (2018) Dynamic magnetometer calibration and alignment to inertial sensors by Kalman filtering. IEEE Trans Control Syst Technol 26(2):716–723

    Article  Google Scholar 

  • Xu Q, Li X, Chan CY (2018) Enhancing localization accuracy of MEMS-INS/GPS/In-vehicle sensors integration during GPS outages. IEEE Trans Instrum Meas 67(8):1966–1978

    Article  Google Scholar 

  • Yang B, Lu H, Chen L (2010) BPNN and RBFNN based modeling analysis and comparison for cement calcination process. In: IWACI 2010, 3rd international workshop on advanced computational intelligence, Suzhou, Jiangsu, China, 25–27 August, pp 101–106

  • Zhang Y, Shen C, Tang J, Liu J (2018) Hybrid algorithm based on MDF-CKF and RF for GPS/INS system during GPS outages (April 2018). IEEE Access 6:35343–35354

    Article  Google Scholar 

  • Zheng J, Zhao W, Han B, Wen Y (2016) Integrating extreme learning machine with Kalman filter to bridge GPS outages. In: ICISCE 2016, 3rd international conference on information science and control engineering, Beijing, China, 8–10 July, pp 420–424

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation (61571148, 61871143), Fundamental Research for the Central University (HEUCFG201823, 3072019CF0402), Heilongjiang Natural Science Foundation (LH2019F006), Research and Development Project of Application Technology in Harbin (2017R-AQXJ095).

Author information

Authors and Affiliations

  1. College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin, 150001, China

    Zongkai Wu & Wei Wang

Authors
  1. Zongkai Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Z., Wang, W. INS/magnetometer integrated positioning based on neural network for bridging long-time GPS outages. GPS Solut 23, 88 (2019). https://doi.org/10.1007/s10291-019-0877-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10291-019-0877-4

Keywords

Search

Navigation