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Comparison of Different Measurement Spaces for Spatio–Temporal Recurrent Track–Before–Detect Algorithm

  • Przemysław Mazurek
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 102)

Summary

Track–Before–Detect (TBD) algorithms are used in object tracking applications. TBD algorithms are used for tracking of a low–SNR objects. Selection of the measurement space is the one of the most important factors that significantly influences on the tracking results. In this paper a few measurement spaces are compared using Monte Carlo numerical tests. The best obtained results are for the angle–only measurement space that is signal shape oriented.

Keywords

Detect Algorithm Tracking System Measurement Space Tracking Algorithm Distance Error 
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.

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References

  1. 1.
    Blackman, S.: Multiple–Target Tracking with Radar Applications. Artech House, Boston (1986)Google Scholar
  2. 2.
    Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House, Boston (1999)zbMATHGoogle Scholar
  3. 3.
    Brookner, E.: Tracking and Kalman Filtering Made Easy. Wiley Interscience, Hoboken (1998)CrossRefGoogle Scholar
  4. 4.
    Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME–Journal of Basic Engineering 82, Series D, 35–46 (1960)Google Scholar
  5. 5.
    Stone, L.D., Barlow, C.A., Corwin, T.L.: Bayesian Multiple Target Tracking. Artech House, Boston (1999)zbMATHGoogle Scholar
  6. 6.
    Metropolis, N., Ulam, S.: The Monte Carlo Method. Journal of the American Statistical Association 44(247), 335–341 (1949)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Robert, C.P., Casella, G.: Monte Carlo Statistical Methods. Springer, Heidelberg (2004)zbMATHGoogle Scholar
  8. 8.
    Bar–Shalom, Y.: Multitarget–Multisensor Tracking: Applications and Advances, vol. II. Artech House, Boston (1992)Google Scholar
  9. 9.
    Doucet, A., de Freitas, N., Gordon, N., Smith, A.: Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  10. 10.
    Ristic, B., Arulampalam, S., Gordon, N.: Beyound the Kalman Filter: Particle Filters for Tracking Applications. Artech House, Boston (2004)Google Scholar
  11. 11.
    Boers, Y., Ehlers, F., Koch, W., Luginbuhl, T., Stone, L.D., Streit, R.L.: Track Before Detect Algorithm. EURASIP Journal on Advances in Signal Processing (2008)Google Scholar
  12. 12.
    O’Hagan, A., Forster, J.: Kendall’s Advanced Theory of Statistics, vol. 2B. Bayesian Inference, Arnold (2003)Google Scholar
  13. 13.
    Mazurek, P.: Implementation of spatio–temporal Track–Before–Detect algorithm using GPU. Pomiary Automatyka Kontrola 55(8), 657–659 (2009)Google Scholar
  14. 14.
    Mazurek, P.: Likelihood functions synthesis for multitarget multiple–sensor tracking applications using GPGPU. Pomiary Automatyka Kontrola 56(7), 662–664 (2010)Google Scholar
  15. 15.
    Mazurek, P.: Optimization of Track–Before–Detect Systems with Decimation for GPGPU. Pomiary Automatyka Kontrola 56(12), 1523–1525 (2010)Google Scholar
  16. 16.
    Mazurek, P.: Optimization of bayesian Track–Before–Detect algorithms for GPGPUs implementations. Electrical Review R 86(7), 187–189 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Przemysław Mazurek
    • 1
  1. 1.Department of Signal Processing and Multimedia EngineeringWest–Pomeranian University of TechnologySzczecinPoland

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