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Background noise distribution for noise object tracking in Track-Before-Detect systems using minimal chi-square statistic

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Treść / Zawartość
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Warianty tytułu
Konferencja
Computer Applications in Electrical Engineering 2012 (23-24.04.2012; Poznań, Polska)
Języki publikacji
EN
Abstrakty
EN
Tracking of the noise signal in noise measurements needs special techniques. The difference between object and background noise is defined, using the noise distribution. The proposed technique is based on the model of the background noise. The window based approach is used for input signal preprocessing. The comparison of two distributions - empirical and observed is used. The global distribution is obtained using all measurements and the observed distribution is computed in the window area only. Minimal chi-square statistic is used as comparison criteria and results are processed by Spatio-Temporal Track-Before-Detect algorithm for tracking of the dynamic of the object and improved signal denoising. A few examples are shown for different objects that show possibilities of the proposed solution. Mean value suppression is possible using comparison of both distributions, what is important in application where the background estimation is not ideal.
Rocznik
Tom
Strony
431--441
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
  • West-Pomeranian University of Technology, Szczecin 71-126 Szczecin, ul. 26. Kwietnia 10, orinoco@zut.edu.pl
Bibliografia
  • [1] Aczel A. D., Complete Business Statistics, Irwin, 1993.
  • [2] Bar-Shalom Y., Multitarget-Multisensor Tracking: Applications and Advances, vol. II, Artech House, 1992.
  • [3] Blackman S., Multiple-Target Tracking with Radar Applications. Artech House, 1986.
  • [4] Blackman S., Popoli R., Design and Analysis of Modern Tracking Systems, Artech House, 1999.
  • [5] 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.
  • [6] Mazurek P., Chi-square statistic for noise objects tracking in Track-Before-Detect systems, Poznań University of Technology Academic Journals - Electrical Engineering, nr 71, 177-184, 2012.
  • [7] Mazurek P., GPGPU-based implementation of chi-square Spatio-Temporal Track-Before-Detect algorithm, Measurement Automation and Monitoring, vol. 58 no. 7, 590-592, 2012.
  • [8] Mazurek P., Noise suppression for Track-Before-Detect algorithms using spatial noise estimators, 16. Konferencja Naukowo-Techniczna "Zastosowania Komputerów w Elektrotechnice" ZKwE'2011 Poznań, 281-282, 2011.
  • [9] Mazurek P., 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, Springer Verlag, 157-164, 2011.
  • [10] Mazurek P., 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, Springer Verlag, 511-518, 2011.
  • [11] Mazurek P., Optimization of bayesian Track-Before-Detect algorithms for GPGPUs implementations, Electrical Review, R. 86 no. 7/2010, 187-189, 2010.
  • [12] Mazurek P., Impulse noise of small and point targets in recurrent Track-Before- Detect algorithms, Poznań University of Technology Academic Journals - Electrical Engineering, nr 61, 53-62, 2010.
  • [13] Mazurek P., Optimization of Track-Before-Detect systems for GPGPU, Measurement Automation and Monitoring, vol. 56 no. 7, 665-667, 2010.
  • [14] Mazurek P., Optimization of Track-Before-Detect Systems with Decimation for GPGPU, Measurement Automation and Monitoring, vol. 56 no. 12, 1523-1525, 2010.
  • [15] Mazurek P., Suppression of impulse noise in track-before-detect algorithms, Computer Applications in Electrical Engineering, vol. 8, Poznań, 201-211, 2010.
  • [16] Mazurek P., Track-Before-Detect Algorithm for Noise Objects, Measurement Automation and Monitoring, vol. 56 no. 10, 1183-1185, 2010.
  • [17] Mazurek P., Suppression of impulse noise in track-before-detect algorithms using saturation and pulse removal, 15. Konferencja Naukowo-Techniczna "Zastosowania Komputerów w Elektrotechnice" ZKwE'2010 Poznań, 301-302, 2010.
  • [18] Mazurek P., Improving response of recurrent Track-Before-Detect algorithms for small and point targets, 14. Konferencja Naukowo-Techniczna "Zastosowania Komputerów w Elektrotechnice" ZKwE'2009 Poznań, 351-352, 2009.
  • [19] Ristic B., Arulampalam S., Gordon N., Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, 2004.
  • [20] Stone L. D., Barlow C. A., Corwin T. L. Bayesian Multiple Target Tracking. Artech House, 1999.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-02f1c61d-d186-4cf7-9cbb-fe0e4c9e974b
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