Skip to main content
SpringerLink
Log in

Estimating Vehicle State by GPS/IMU Fusion with Vehicle Dynamics

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

In this paper, a low-cost navigation system with high integrity and reliability is proposed. A high-integrity estimation filter is proposed to obtain a high-accuracy state estimate. The filter utilizes a vehicle velocity constraint measurement to enhance the accuracy of the estimate. Two estimation filters, the extended Kalman filter (EKF) and the extended information filter (EIF), are designed and compared to obtain the estimate of the vehicle state. An instrumentation system that consists of a microcontroller, GPS receiver, IMU, velocity encoder, and Zigbee transceiver is used. The microcontroller provides a vehicle navigation solution at 50 Hz by fusing the measurements of the IMU and GPS receiver using the proposed filter design. Extensive experimental tests are conducted to verify the accuracy of the proposed algorithm. These results are processed with and without the velocity constraints. The estimation accuracy improvement with the addition of the velocity constraints is shown. A more than 16 % reduction in the computational time is demonstrated when using the EIF in comparison to the EKF approach.

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

Similar content being viewed by others

References

  1. Sahawneh, L.R., Al-Jarrah, M.A., Assaleh, K., Abdel-Hafez, M.F.: Real-time implementation of GPS aided low cost strapdown inertial navigation system. J. Intell. Robot. Syst. 61(1–4), 527–544 (2011)

    Article  Google Scholar 

  2. Sukkarieh, S.: Low cost, high integrity, aided inertial navigation systems for autonomous land vehicles. Ph.D. dissertation, Mechanical and Mechatronic Engineering, Australian Centre for Field Robotics, The University of Sydney, Sydney, Australia (2000)

  3. Abdel-Hafez, M.F.: The autocovariance least squares technique for GPS measurement noise estimation. IEEE Trans. Veh. Technol. 59(2), 574–588 (2010)

    Article  Google Scholar 

  4. Noureldin, A., Karamat, T.B., Eberts, M.D., El-Shafie, A.: Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications. IEEE Trans. Veh. Technol. 58(3), 1077–1096 (2009)

    Article  Google Scholar 

  5. Schelling, R.: A low-cost angular rate sensor for automotive applications in surface micromachining technology. In: 3rd Annual International Conference on Advanced Microsystems for Automotive Applications Proceedings (1999)

  6. Belanovié, P., Valerio, D., Paier, A., Zemen, T., Ricciato, F., Mecklenbräuker, C.F.: On wireless links for vehicle-to-infrastructure communications. IEEE Trans. Veh. Technol. 59(1), 269–282 (2010)

    Article  Google Scholar 

  7. Kinney, P.: ZigBee Technology: Wireless Control that Simply Works. Communications Design Conference (2003)

  8. Biswas, S., Tatchikou, R., Dion, F.: Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety. IEEE Commun. Magaz. 44(1), 74–82 (2006)

    Article  Google Scholar 

  9. Lahrech, A., Boucher, C., Noyer, J.-C.: Accurate vehicle positioning in urban areas. In: IMTC 2007—IEEE Instrumentation and Measurement Technology Conference, Warsaw, Poland, 1–3 May 2007

  10. Che-Chung, L., Chi-Wei, L., Dau-Chen, H., Yung-Hsin, C.: Design a support vector machine-based intelligent system for vehicle driving safety warning. In: 11th International IEEE Conference on Intelligent Transportation Systems, pp. 938–943, 12–15 Oct 2008

  11. Hightower, D.: Wireless technology advances crash avoidance. Microwaves & RF, pp. 22 (2010)

  12. Santa, J., Toledo-Moreo, R., Zamora-Izquierdo, M.A., Ubeda, B., Gomez-Skarmeta, A.F.: An analysis of communication and navigation issues in collision avoidance support systems. Transp. Res. Part C. Emerg. Technol. 18(3), 351–366 (2010)

    Article  Google Scholar 

  13. Bonnabel, S., Salaün, E.: Design and prototyping of a low-cost vehicle localization system with guaranteed convergence properties. Control. Eng. Pract. 19, 591–601 (2011)

    Article  Google Scholar 

  14. Brandt, A., Gardner, J.F.: Constrained navigation algorithms for strapdown inertial navigation systems with reduced set of sensors. In: Proceedings of the American Control Conference (1998)

  15. Dissanayake, G., Sukkarieh, S.: The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications. IEEE Trans. Robot. Autom. 17(5), 731–747 (2001)

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Kong, X.: INS algorithm using quaternion model for low cost IMU. Robot. Auton. Syst. 46, 221–246 (2004)

    Article  Google Scholar 

  18. Julier, S.J., Uhlmann, J.K., Durrant-Whyte, H.F.: A new approach for filtering nonlinear systems. In: Proceedings of the 1995 American Control Conference, pp. 1628–1632. Seattle, WA (1995)

  19. Omni Instruments http://www.omniinstruments.co.uk/gyro/MIDGII.htm (2013). Accessed 14 July 2013

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, American University of Sharjah, Sharjah, UAE

    Kamal Saadeddin, Mamoun F. Abdel-Hafez & Mohammad Amin Jarrah

Authors
  1. Kamal Saadeddin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mamoun F. Abdel-Hafez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Amin Jarrah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mamoun F. Abdel-Hafez.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Saadeddin, K., Abdel-Hafez, M.F. & Jarrah, M.A. Estimating Vehicle State by GPS/IMU Fusion with Vehicle Dynamics. J Intell Robot Syst 74, 147–172 (2014). https://doi.org/10.1007/s10846-013-9960-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-013-9960-1

Keywords

Search

Navigation