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Optimization of data access in GPGPU based implementations of Spatio-Temporal Track-Before-Detect algorithm

Autorzy
Identyfikatory
Warianty tytułu
Konferencja
Computer Applications in Electrical Engineering 2012 (23-24.04.2012; Poznań, Polska)
Języki publikacji
EN
Abstrakty
EN
Tracking systems need accurate and fast computations. Track-Before-Detect (TBD) algorithms are applied if the signal is weak (e.g. SNR<1). Spatio-Temporal TBS algorithm uses IIR-based architecture and there are two filtering parts related to the multidimensional processing. Computation of TBD algorithms needs for example GPGPU devices. The main limitations of GPGPU are memory bus bottleneck and limited availability of fast internal shared memory. A few memory optimization techniques are presented. The new technique based on the dual representation of the state space data in proposed. The compression technique allows application of 8-bit representation for state space and floating point state space together. Floating point values are used for areas where the object is located. The TBD system tracks objects and predicts signal data representation together.
Rocznik
Tom
Strony
185--192
Opis fizyczny
Bibliogr. 22 poz., rys.
Twórcy
autor
  • West-Pomeranian University of Technology, Szczecin
Bibliografia
  • [1] Bar-Shalom Y., Multitarget-Multisensor Tracking: Applications and Advances, vol. II, Artech House, 1992.
  • [2] Blackman S., Multiple-Target Tracking with Radar Applications. Artech House, 1986.
  • [3] Blackman S., Popoli R., Design and Analysis of Modern Tracking Systems, Artech House, 1999.
  • [4] 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.
  • [5] Farber R., CUDA Application Design and Development. Morgan Kaufmann, 2011.
  • [6] Kirk D.B., Hwu W.W., Programming Massively Parallel Processors: A Hands-on Approach. Morgan Kaufmann, 2010.
  • [7] 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.
  • [8] 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.
  • [9] 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.
  • [10] Mazurek P., Optimization of bayesian Track-Before-Detect algorithms for GPGPUs implementations, Electrical Review, R. 86 no. 7/2010, 187-189, 2010.
  • [11] 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.
  • [12] Mazurek P., Optimization of Track-Before-Detect systems for GPGPU, Measurement Automation and Monitoring, vol. 56 no. 7, 665-667, 2010.
  • [13] Mazurek P., Optimization of Track-Before-Detect Systems with Decimation for GPGPU, Measurement Automation and Monitoring, vol. 56 no. 12, 1523-1525, 2010.
  • [14] Mazurek P., Suppression of impulse noise in track-before-detect algorithms, Computer Applications in Electrical Engineering, vol. 8, Poznań, 201-211, 2010.
  • [15] Mazurek P., Track-Before-Detect Algorithm for Noise Objects, Measurement Automation and Monitoring, vol. 56 no. 10, 1183-1185, 2010.
  • [16] 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.
  • [17] 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.
  • [18] CUDA C Best Practices Guide v4.0, NVIDIA, 2011.
  • [19] NVIDIA CUDA C Programming Guide v4.0. NVIDIA, 2011.
  • [20] Ristic B., Arulampalam S., Gordon N., Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, 2004.
  • [21] Sanders J., Kandrot E., CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley, 2010.
  • [22] Stone L. D., Barlow C. A., Corwin T. L. Bayesian Multiple Target Tracking. Artech House, 1999.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-94a0e163-132e-4f54-a137-d5437752bd6f
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