A Simulation Framework for UAV Sensor Fusion

  • Enrique Martí
  • Jesús García
  • Jose Manuel Molina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)


The design of complex fusion systems requires experimental analysis, following the classical structure of experiment design, data acquisition, experiment execution and analysis of the obtained results. We present here a framework with simulation capabilities for sensor fusion in aerial vehicles. Thanks to its abstraction level it only requires a few high level properties for defining a whole experiment. Its modular design offers flexibility and makes easy to complete its functionality. Finally, it includes a set of tools for fast development and more accurate analysis of the experimental results.


sensor fusion simulation framework unmanned air vehicle 


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  1. 1.
    Gade, K.: NAVLAB, a Generic Simulation and Post-processing Tool for Navigation. European Journal of Navigation 2(4), 51–59 (2004)Google Scholar
  2. 2.
    Rodriguez, A.L., et al.: Real time sensor acquisition platform for experimental UAV research. In: IEEE/AIAA 28th DASC 2009, pp. 5.C.5-1–5.C.5-10 (October 2009)Google Scholar
  3. 3.
    Aerospace Toolbox - MATLAB. The MathWorks, (Cited: 03 15, 2010)
  4. 4.
    Kurnaz, S., Cetin, O., Kaynak, O.: Fuzzy Logic Based Approach to Design of Flight Control and Navigation Tasks for Autonomous Unmanned Aerial Vehicles. Journal of Intelligent and Robotic Systems 54(1-3), 229–244 (2009)CrossRefGoogle Scholar
  5. 5.
    García, J., et al.: Data fusion architectures for autonomous vehicles using heterogeneous sensors. In: 1st ESA NAVITEC. Noordwikj, Holland (December 2006)Google Scholar
  6. 6.
    Wagner, J.F., Wienekeb, T.: Integrating satellite and inertial navigation—conventional and new fusion approaches. Control Engineering Practice 11(5), 543–550 (2003)CrossRefGoogle Scholar
  7. 7.
    van der Merwe, R., Wan, E., Julier, S.: Sigma Point Kalman Filters for Nonlinear Estimation and Sensor Fusion: Applications to Integrated Navigation. In: AIAA Guidance, Navigation and Controls Conference, Providence, USA (August 2004)Google Scholar
  8. 8.
    Crassidis, J.: Sigma-Point Kalman Filtering for Integrated GPS and Inertial Navigation. IEEE Trans. on AES 42(2) (April 2006)Google Scholar
  9. 9.
    Chiang, K.W., Huang, Y.W.: An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications. Applied Soft Computing 8(1), 722–733 (2008)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Hartikainen, J., Sarkka, S.: Optimal filtering with Kalman filters and smoothers-a Manual for Matlab toolbox EKF/UKF (2007),
  11. 11.
    Chen, L., et al.: PFLib - An Object Oriented MATLAB Toolbox for Particle Filtering. Department of Statistics - Colorado State University (2007), (Cited: 03 14, 2010)

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Enrique Martí
    • 1
  • Jesús García
    • 1
  • Jose Manuel Molina
    • 1
  1. 1.Group of Applied Artificial IntelligenceUniversidad Carlos III de MadridMadrid(Spain)

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