A Sigma-Point Kalman Filter for Remote Sensing of Updrafts in Autonomous Soaring

  • Martin StolleEmail author
  • Yoko Watanabe
  • Carsten Döll


Autonomous soaring is a promising approach to augment the endurance of small UAVs. Most of the existing work on this field relies on accelerometers and/or GPS receivers to sense thermals in the proximity of the vehicle. However, thermal updrafts are often visually indicated by cumulus clouds that are well characterized by their sharp baselines. This paper focuses on a cloud mapping algorithm which estimates the 3D position of cumulus clouds. Using the meteorological fact of a uniform cloud base altitude a state-constrained sigma-point Kalman filter (SCSPKF) is developed. A method of using the resulting cloud map and its uncertainty in the path planning task to realize a soaring flight to a given wayoint is presented as a perspective of this work.


Remote Sensing Path Planning Extend Kalman Filter Cloud Base Cumulus Cloud 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allen, M.J., Lin, V.: Guidance and Control of an Autonomous Soaring UAV with Flight TTest Results. In: 45th AIAA Aerospace Sciences and Meeting and Exhibit (January 2007)Google Scholar
  2. 2.
    Andersson, K., Kaminer, I., Dobrokhodov, V., Cichella, V.: Thermal Centering Control for Autonomous Soaring; Stability Analysis and Flight Test Results. Journal of Guidance Navigation and Control 35, 963–975 (2012)CrossRefGoogle Scholar
  3. 3.
    Akhtar, N., Whidborne, J.F., Cooke, A.K.: Real-time trajectory generation technique for dynamic soaring UAVs. In: Proceedings of the UKACC International Conference on Control (2008)Google Scholar
  4. 4.
    Allen, M.J.: Updraft model for development of autonomous soaring uninhabited air vehicles. In: 44th AIAA Aerospace Sciences Meeting and Exhibit, American Institute for Aeronautics and Astronautics, AIAA (January 2006)Google Scholar
  5. 5.
    Edwards, D.J., Silberberg, L.M.: Autonomous Soaring: The Montague Cross-Country Challenge. AIAA Journal of Aircraft 47, 1763–1769 (2010)CrossRefGoogle Scholar
  6. 6.
    Kahveci, N.: Robust Adaptive Control For Unmanned Aerial Vehicles. PhD thesis, University of Southern California - Faculty of the Graduate School, USA (2007)Google Scholar
  7. 7.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-8, 679–698 (1986)Google Scholar
  8. 8.
    Kleinschmidt, E.: Handbuch der Meteorologischen Instrumente und ihrer Auswertung. Verlag von Julius Springer (1935)Google Scholar
  9. 9.
    Pagen, D.: Understanding the sky. Dennis Pagen (February 1992)Google Scholar
  10. 10.
    Yaakov, T.K., Bar-Shalom, X., Li, R.: Estimation with Applications To Tracking and Navigation. John Wiley and Sons, Inc. (2001)Google Scholar
  11. 11.
    Ponda, S.S.: Trajectory Optimization for Target Localization Using Small Unmanned Aerial Vehicles. Master’s thesis, Massachusetts Institute of Technology (September 2008)Google Scholar
  12. 12.
    Julier, S.J.: A skewed approach to filtering. In: SPIE Conference on Signal and Data Processing of Small Targets, vol. 3373, pp. 271–282. SPIE (1998)Google Scholar
  13. 13.
    Julier, S.J., Uhlman, J.K.: A New Extension of the Kalman Filter to Nonlinear systems. In: Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI (April 1997)Google Scholar
  14. 14.
    Van der Merwe, R.: The square-root unscented Kalman filter for state and parameter estimation. In: 2001 IEEE Acoustics, Speech, and Signal Processing, Proceedings (ICASSP 2001), vol. 6, pp. 3461–3464 (May 2001)Google Scholar
  15. 15.
    Van der Merwe, R., Wan, E.A.: The unscented Kalman filter for nonlinear estimation. In: The IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC, pp. 153–185. IEEE (October 2000)Google Scholar
  16. 16.
    Rhudy, M., Gu, Y.: Understanding Nonlinear Kalman Filters, Fart II: An Implementation Guide. Interactive Robotics Letters (2013)Google Scholar
  17. 17.
    Langelaan, J.W.: State estimation for autonomous flight in cluttered environments. PhD thesis, Stanford University (March 2006)Google Scholar
  18. 18.
    Montemerlo, M., Thrun, S.: FastSLAM: A Scalable Method For The Simulations Localization and Mapping Problem in Robotics. Springer (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Systems Control and Flight Dynamics (DCSD)The French Aerospace Laboratory (ONERA)Toulouse Cedex 4France

Personalised recommendations