Monte Carlo Analysis of Local Cross–Correlation ST–TBD Algorithm

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11536)


The Track–Before–Detect (TBD) algorithms allow the estimation of the state of an object, even if the signal is hidden in the background noise. The application of local cross–correlation for the modified Information Update formula improves this estimation for extended objects (tens of cells in the measurement space) compared to the direct application of the Spatio–Temporal TBD (ST–TBD) algorithm. The Monte Carlo test was applied to evaluate algorithms by using a variable standard deviation of the additive Gaussian noise. The proposed solution does not require prior knowledge of the size or measured values of the object. Mean Absolute Error for the proposed algorithm is much lower, close to zero to about 0.8 standard deviation, which is not achieved for the ST–TBD.


Track–Before–Detect Tracking Algorithm analysis Monte Carlo Cross–correlation 



This work is supported by the UE EFRR ZPORR project Z/2.32/I/1.3.1/267/05 “Szczecin University of Technology – Research and Education Center of Modern Multimedia Technologies” (Poland).


  1. 1.
    Abualigah, L.: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering. Studies in Computational Intelligence. Springer, Cham (2018). Scholar
  2. 2.
    Abualigah, L., Khader, A.T., Said Hanandeh, E.: A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng. Appl. Artif. Intell. 73, 111–125 (2018)CrossRefGoogle Scholar
  3. 3.
    Abualigah, L., Khader, A.T., Said Hanandeh, E.: Hybrid clustering analysis using improved krill herd algorithm. Appl. Intell. 48(11), 4047–4071 (2018)CrossRefGoogle Scholar
  4. 4.
    Abualigah, L.M., Khader, A.T.: Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J. Supercomput. 73(11), 4773–4795 (2017)CrossRefGoogle Scholar
  5. 5.
    Blackman, S.: Multiple-Target Tracking with Radar Applications. Artech House, Dedham (1986)Google Scholar
  6. 6.
    Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems. Artech House, Dedham (1999)zbMATHGoogle Scholar
  7. 7.
    Boers, Y., Ehlers, F., Koch, W., Luginbuhl, T., Stone, L.D., Streit, R.L.: Track before detect algorithms. J. Adv. Signal Process. 2008, 2 (2008). Article ID 413932, Hindawi Publishing Corporation EURASIPCrossRefzbMATHGoogle Scholar
  8. 8.
    Chapman, B., Jost, G., Pas, R.V.D.: Using OpenMP: Portable Shared Memory Parallel Programming (Scientific and Engineering Computation). The MIT Press, Cambridge (2007)Google Scholar
  9. 9.
    Clerc, M.: From theory to practice in particle swarm optimization. In: Panigrahi, B.K., Shi, Y., Lim, H.M. (eds.) Handbook of Swarm Intelligence. Adaptation, Learning, and Optimization, pp. 3–36. Springer, Heidelberg (2011). Scholar
  10. 10.
    Dragovic, M.: Velocity Filtering for Target Detection and Track Initiation, vol. DSTO-TR-1406. Weapons Systems Division, Systems Sciences Laboratory (2003)Google Scholar
  11. 11.
    Farber, R.: CUDA Application Design and Development. Morgan Kaufmann, San Francisco (2011)Google Scholar
  12. 12.
    Karniadakis, G., Kirby, R.: Parallel Scientific Computing in C++ and MPI. Cambridge University Press, New York (2003)CrossRefGoogle Scholar
  13. 13.
    Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Hoboken (1990)CrossRefGoogle Scholar
  14. 14.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 - International Conference on Neural Networks, vol. 4, pp. 1942–1948, November 1995Google Scholar
  15. 15.
    King, R.S.: Cluster Analysis and Data Mining: An Introduction. Mercury Learning & Information, Dulles (2014)Google Scholar
  16. 16.
    Mazurek, P.: Comparison of different measurement spaces for spatio–temporal recurrent track–before–detect algorithm. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 3. AISC, vol. 102, pp. 157–164. Springer, Heidelberg (2011). Scholar
  17. 17.
    Mazurek, P.: Parallel distributed downsampled spatio-temporal track-before-detect algorithm. In: 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 119–124, September 2014Google Scholar
  18. 18.
    Mazurek, P.: Preprocessing using maximal autocovariance for spatio-temporal track-before-detect algorithm. In: Choras, R.S. (ed.) Image Processing and Communications Challenges 5. AISC, vol. 233, pp. 45–54. Springer, Heidelberg (2014). Scholar
  19. 19.
    Mazurek, P.: Noise objects tracking using multiple order statistics and spatio-temporal track-before-detect algorithm. In: Choraś, R. (ed.) IP&C 2016. AISC, vol. 525, pp. 112–119. Springer, Cham (2017). Scholar
  20. 20.
    Metropolis, N.: The Beginning of the Monte Carlo Method. Los Alamos Science (1987).
  21. 21.
    Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley, Upper Saddle River (2010)Google Scholar
  22. 22.
    Scott, T.A., Nilanjan, R.: Biomedical Image Analysis: Tracking. Morgan & Claypool, San Rafael (2005)Google Scholar
  23. 23.
    Spall, J.C.: Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control. Willey, Hoboken (2003)CrossRefGoogle Scholar
  24. 24.
    Stone, L., Barlow, C., Corwin, T.: Bayesian Multiple Target Tracking. Artech House, Norwood (1999)zbMATHGoogle Scholar
  25. 25.
    Torstensson, J., Trieb, M.: Particle Filtering for Track Before Detect Applications. Master’s thesis, Division of Automatic Control, Department of Electrical Engineering, Linköping University (2005)Google Scholar
  26. 26.
    Zhang, T., Li, M., Zuo, Z., Yang, W., Sun, X.: Moving dim point target detection with three-dimensional wide-to-exact search directional filtering. Pattern Recogn. Lett. 28, 246–253 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Signal Processing and Multimedia EngineeringWest Pomeranian University of Technology SzczecinSzczecinPoland

Personalised recommendations