Skip to main content
Log in

Development of a GPU Accelerated Terrain Referenced UAV Localization and Navigation Algorithm

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

Abstract

This study focuses on localization and navigation of Unmanned Air Vehicles (UAVs) based on digital terrain map data. The solution to the Terrain Referenced Localization and Navigation (TERELONA) or Terrain Referenced Navigation (TRN) is described by using particle filter. In many UAV applications one of the most important points is to provide accurate location information continuously. TERELONA system can supply the air vehicle with the accurate position information with a bounded error. In this paper, the particle filtering method as an implementation of Bayesian approach to the terrain referenced localization and navigation is described. The radar altimeter measurements are used as an implicit representation of aircraft position. Whenever new measurements are taken from radar altimeter, they are compared to the Digital Terrain Map (DTM) data in order to fix a position. The solution is represented, in a Bayesian framework, by a set of particles with their corresponding weights. We have developed the terrain referenced localization and navigation algorithm based on the particle approximation. The proposed algorithm, which is developed in CUDATM, is also tested on the GPU environment using GPUmat software architecture. Thus, we can cope with the computational load of the very large initial horizontal position errors. The proposed algorithm has been implemented in MATLABTM environment and evaluated on simulated data. Simulations are conducted over an ASTER GDEM product which belongs to a region in northwest of Turkey. The simulation results are provided.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Global Positioning System Standard Positioning Service Performance Standard Document: 4th Edition September 2008. U.S Government Official Performance Standards & Specifications. http://www.gps.gov/technical/ps/ (2008). Accessed 15 January 2012

  2. Johnson, N., Tang, W., Howell, G.: Terrain aided navigation using maximum a posteriori estimation. In: IEEE Position Location and Navigation Symposium (1990)

  3. Baker, W.R., Clem, R.W.: Terrain contour matching (TERCOM) primer. Tech. Rep. ASP-TR-7-61. Aeronaut. Syst. Div., Wright-Patterson AFB, OH (1977)

  4. Hicks, S.: Advanced cruise missile guidance system description. Aerospace and Electronics Conference (NAECON 1993) 1, 355–361 (1993). doi:10.1109/NAECON.1993.290941

    Article  Google Scholar 

  5. Ekutekin, V.: Navigation and control studies on cruise missiles. PhD thesis in Mechanical Engineering, Middle East Technical University, Ankara, Turkey (2007)

  6. Hostetler, L., Andreas, R.: Nonlinear Kalman filtering techniques for terrain-aided navigation. IEEE Trans. Automat. Contr. 28(3), 315–323 (1983)

    Article  MATH  Google Scholar 

  7. Henley, A.J.: Terrain aided navigation: current status, techniques for flat terrain and reference data requirements. In: Position Location and Navigation Symposium, 1990, The 1990’s A Decade of Excellence in the Navigation Sciences, IEEE PLANS ’90, pp. 608–615 (1990)

  8. Hollowell, J.: Heli/SITAN: a terrain referenced navigation algorithm for helicopters. In: Position Location and Navigation Symposium, 1990. The 1990’s A Decade of Excellence in the Navigation Sciences. IEEE PLANS ’90, pp. 616–625 (1990)

  9. Cowie, M., Wilkinson, N., Powlesland, R.: Latest development of the TERPROM® Digital Terrain System (DTS). In: Position, Location and Navigation Symposium, 2008 IEEE/ION, pp. 1219–1229 (2008)

  10. Jianchun, X., et al.: Combined terrain aided navigation based on correlation method and parallel Kalman filters. In: 8th International Conference on Electronic Measurement and Instruments, 2007, ICEMI ‘07, pp. 1-145–1-150 (2007)

  11. Bergman, N., Ljung, L., Gustafsson, F.: Terrain navigation using Bayesian statistics. IEEE Contr. Syst. 19(3), 33–40 (1999)

    Article  Google Scholar 

  12. Nygren, I., Magnus, J.: Terrain navigation for underwater vehicles using the correlator method. IEEE J. Ocean. Eng. 29(3), 906–915 (2004)

    Article  Google Scholar 

  13. Flament, M., Lacave, J.N., Fleury, G.: Particle filtering for non-linear sensor fusion: Application to terrain-aided navigation. In: Proceedings of EUCASS’05 Moscow: EADS 1–7 (2005)

  14. Karlsson, R., Gustafsson, F., Karlsson, T.: Particle filtering and Cramer-Rao lower bound for underwater navigation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing Proceedings (ICASSP ’03), vol. 6, pp. VI-65-8, 6–10 April (2003). doi:10.1109/ICASSP.2003.1201619

  15. Qingtang, F., Lincheng, S., Wenseng, C.: Terrain aided navigation using PDAF. In: 2003 IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, Proceedings, vol. 2, pp. 1063–1068 (2003)

  16. Cetin, O., Kurnaz, S., Kaynak, O., Temeltas, H.: Potential field based navigation task for autonomous flight control of UAVs. Int. J. Autom. Contr. 5(1), 1–21 (2011)

    Article  Google Scholar 

  17. Kedong, W., Yang, Y.: Influence of application conditions on terrain-aided navigation. In: 8th World Congress on Intelligent Control and Automation (WCICA), pp. 391–396 (2010)

  18. Smith, A.F.M., Gelfand, A.E.: Bayesian statistics without tears: a sampling-resampling perspective. Am. Stat. 46(2), 84–88 (1992)

    MathSciNet  Google Scholar 

  19. Gordon, N.J., Salmond, D.J., Smith, A.F.M.: A novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proceedings on Radar and Signal Processing 140, 107–113 (1993)

    Article  Google Scholar 

  20. Bergman, N.: Recursive Bayesian estimation: navigation and tracking applications. Ph.D. thesis, Linköping University, Dissertations No. 579 (1999)

  21. Doucet, A., Freitas, N., Gordon, N.: Sequential Monte Carlo methods in practice. Springer, New York (2001)

    MATH  Google Scholar 

  22. Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge, MA (2005)

    MATH  Google Scholar 

  23. Gustafsson, F., Gunnarsson, F., et al.: Particle filters for positioning, navigation, and tracking. IEEE Trans. Signal Process. 50(2), 425–437 (2002)

    Article  Google Scholar 

  24. Hol, J.D., Schon, T.B., Gustafsson, F.: On Resampling algorithms for particle filters. In: Nonlinear Statistical Signal Processing Workshop, IEEE, pp. 79–82 (2006)

  25. NVIDIA: NVIDIA CUDA C Getting Started Guide. NVIDIA Corporation. http://developer.nvidia.com/nvidia-gpu-computing-documentation. Accessed 20 January 2012

  26. NVIDIA: NVIDIA CUDA C Programming Guide. NVIDIA Corporation. Version 4.0. http://developer.com/nvidia-gpu-computing-documentation (2011). Accessed 26 January 2012

  27. CUDATM: NVIDIA Corporation. http://www.nvidia.com/object/cuda_home.html# (2012). Accessed 10 January 2012

  28. GPUmat: User Guide, Version 0.27. http://gp-you.org/ (2010). Accessed 20 January 2012

  29. GPGPU: General purpose computing on graphics processing units. http://www.gpgpu.org (2012). Accessed 20 January 2012

  30. ASTER: http://asterweb.jpl.nasa.gov (2011). Accessed 10 November 2011

  31. Kurnaz, S., Cetin, O., Kaynak, O.: Fuzzy logic based approach to design flight control and navigation tasks for autonomous UAVs. J. Intell. Robot. Syst. 54, 229–244 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hikmet Yigit.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yigit, H., Yilmaz, G. Development of a GPU Accelerated Terrain Referenced UAV Localization and Navigation Algorithm. J Intell Robot Syst 70, 477–489 (2013). https://doi.org/10.1007/s10846-012-9735-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-012-9735-0

Keywords

Navigation