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Tytuł artykułu

Track-Before-Detect Filter Banks for Noise Object Tracking

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Języki publikacji
EN
Abstrakty
EN
The Track-Before-Detect (TBD) filter banks is proposed for the processing of noise object that are additive to the background noise. Spatio-Temporal TBD algorithm uses the preprocessing of measurement. The modified moving standard deviation filter is applied. The correction of the results for the selection of the highest possible filter banks window is proposed. Position and velocity errors are evaluated numerically for two smoothing coefficients. Monte Carlo test shows that all filter banks allow the tracking if the standard deviation of the background is below 1.3.
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  • Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology, 26-Kwietnia 10, 71-126 Szczecin, Poland
Bibliografia
  • [1] S. Blackman and R. Popoli, Design and Analysis of Modern Tracking Systems. Artech House, 1999.
  • [2] M. Malanowski, “Algorithm for target tracking using passive radar,” Intl. Journal of Electronics and Telecommunications, vol. 58 (4), pp. 345-350, 2012.
  • [3] P. Samczyński, M. Wilkowski, and K. Kulpa, “Trial results on bistatic passive radar using non-cooperative pulse radar as illuminator of opportunity,” Intl. Journal of Electronics and Telecommunications, vol. 58 (2), pp. 171-176, 2012.
  • [4] Y. Bar-Shalom, Multitarget-Multisensor Tracking: Applications and Advances, vol. II. Artech House, 1992.
  • [5] S. Blackman, Multiple-Target Tracking with Radar Applications. Artech House, 1986.
  • [6] Y. Boers, F. Ehlers, W. Koch, T. Luginbuhl, L. Stone, and R. Streit, “Track before detect algorithm,” EURASIP Journal on Advances in Signal Processing, 2008.
  • [7] L. Stone, C. Barlow, and T. Corwin, Bayesian Multiple Target Tracking. Artech House, 1999.
  • [8] P. Mazurek, “Optimization of Bayesian track-before-detect algorithms for GPGPUs implementations,” Electrical Review, vol. 86 (7), pp. 187-189, 2010.
  • [9] Hierarchical track-before-detect algorithm for tracking of amplitude modulated signals, Advances in Intelligent and Soft Computing, vol. 102 - Image Processing and Communications Challenges 3, pp. 511–518, 2011.
  • [10] Track-before-detect algorithm for noise objects, Measurement Automation and Monitoring, vol. 56 (10), pp. 1183-1185, 2010.
  • [11] Comparison of different measurement spaces for spatio-temporal recurrent track-before-detect algorithm,” Advances in Intelligent and Soft Computing, vol. 102 - Image Processing and Communications Challenges 3, pp. 157-164, 2011.
  • [12] Chi-square statistic for noise object tracking in track-beforedetect systems,” Poznań University of Technology Academic Journals - Electrical Engineering, vol. 71, pp. 177-184, 2012.
  • [13] Application of dot product for track-before-detect tracking of noise objects,” Poznań University of Technology Academic Journals - Electrical Engineering, vol. 76, pp. 101-107, 2013.
  • [14] Code reordering using local random extraction and insertion (LREI) operator for GPGPU-based track-before-detect systems,” Soft Computing, vol. 18 (6), pp. 1095-1106, 2013.
  • [15] Estimation track-before-detect motion capture systems state space spatial component,” Lecture Notes in Computer Science, vol. 4673 (Computer Analysis of Images and Patterns), pp. 149-156, 2007.
  • [16] Estimation of state-space spatial component for cuboid trackbefore-detect motion capture systems,” Lecture Notes in Computer Science, vol. 5337 (Computer Vision and Graphics International Conference ICCVG 2008), pp. 451–460, 2009.
  • [17] M. John, M. Inggs, and D. Petri, “Real time processing of networked passive coherent location radar system,” Intl. Journal of Electronics and Telecommunications, vol. 57 (3), pp. 363-368, 2011.
Typ dokumentu
Bibliografia
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