Sequential Monte Carlo particle filters (PFs) are useful for estimating nonlinear non-Gaussian dynamic system parameters. As these algorithms are recursive, their real-time implementation can be computationally complex. In this paper, we analyze the bottlenecks in existing parallel PF algorithms, and we propose a new approach that integrates parallel PFs with independent Metropolis-Hastings (PPF-IMH) algorithms to improve root mean-squared estimation error performance. We implement the new PPF-IMH algorithm on a Xilinx Virtex-5 field programmable gate array (FPGA) platform. For a one-dimensional problem and using 1,000 particles, the PPF-IMH architecture with four processing elements utilizes less than 5% Virtex-5 FPGA resources and takes 5.85 μs for one iteration. The algorithm performance is also demonstrated when designing the waveform for an agile sensing application.

Original languageEnglish (US)
Title of host publication2010 IEEE Workshop on Signal Processing Systems, SiPS 2010 - Proceedings
Number of pages6
StatePublished - Dec 27 2010
Event2010 IEEE Workshop on Signal Processing Systems, SiPS 2010 - San Francisco, CA, United States
Duration: Oct 6 2010Oct 8 2010

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
ISSN (Print)1520-6130


Other2010 IEEE Workshop on Signal Processing Systems, SiPS 2010
Country/TerritoryUnited States
CitySan Francisco, CA


  • FPGA
  • Independent Metropolis-Hastings algorithm
  • Parallel architecture
  • Particle filter

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture


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