7 Scopus citations


Probability hypothesis density (PHD) filtering, implemented using particle filters, is a Bayesian technique used to non-linearly track multiple objects. In this paper, we propose a new approach based on PHD particle filters (PHD-PF) to automatically track the number of magnetoencephalography (MEG) neural dipole sources and their unknown states. In particular, by separating the MEG measurements using independent component analysis, PHD-PF is applied in a closed-loop to first estimate the number of sources and then recover their amplitude, location and orientation. We also reduce the processing time and computational complexity by employing window-based processing and multi-channel decomposition. We simulate the overall system using synthetic data and show that the proposed algorithm has tracking performance similar to existing approaches with significantly fewer number of particles. We also map the algorithm on to a Xilinx Virtex-5 field-programmable gate array (FPGA) platform. The processing period for one iteration using 3,200 particles is only about 314 μs, which makes this implementation suitable for real-time tracking.

Original languageEnglish (US)
Title of host publication2011 IEEE Workshop on Signal Processing Systems, SiPS 2011, Proceedings
Number of pages6
StatePublished - Dec 26 2011
Event2011 IEEE Workshop on Signal Processing Systems, SiPS 2011 - Beirut, Lebanon
Duration: Oct 4 2011Oct 7 2011

Publication series

Name2011 IEEE Workshop on Signal Processing Systems, SiPS 2011, Proceedings


Other2011 IEEE Workshop on Signal Processing Systems, SiPS 2011


  • FPGA implementation
  • PHD filter
  • independent component analysis
  • parallel architecture

ASJC Scopus subject areas

  • Signal Processing


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