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Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing

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Abstract

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 propose a new approach that integrates parallel PFs with independent Metropolis–Hastings (PPF-IMH) resampling algorithms to improve root mean-squared estimation error (RMSE) performance. We implement the new PPF-IMH algorithm on a Xilinx Virtex-5 field programmable gate array (FPGA) platform. For a one-dimensional problem with 1,000 particles, the PPF-IMH architecture with four processing elements uses less than 5% of a Virtex-5 FPGA’s resource and takes 5.85 μs for one iteration. We also incorporate waveform-agile tracking techniques into the PPF-IMH algorithm. We demonstrate a significant performance improvement when the waveform is adaptively designed at each time step with 6.84 μs FPGA processing time per iteration.

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Authors and Affiliations

  1. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

    Lifeng Miao, Jun Jason Zhang, Chaitali Chakrabarti & Antonia Papandreou-Suppappola

Authors
  1. Lifeng Miao
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  2. Jun Jason Zhang
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  3. Chaitali Chakrabarti
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  4. Antonia Papandreou-Suppappola
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Corresponding author

Correspondence to Lifeng Miao.

Additional information

This work was partly supported by NSF under Grant No. 0830799 and MURI Grant AFOSR FA9550-05-1-0443.

The parallel particle filter implementation was discussed in our 2010 IEEE Workshop on Signal Processing Systems paper [1]. This work also presents: the new algorithm and hardware implementation described in more detail (Section 3); the effect of the number of processing elements and number of groups in each processing element on the parallel particle filter algorithm performance (Sections 5.2 and 6.1); the waveform-agile sensing algorithm (Section 4.1), the waveform-agile tracking application (Section 4.2), and its hardware (FPGA) implementation (Section 4.3); new simulation and hardware implementation results on waveform-agile tracking (Sections 5.4 and 6.2).

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Miao, L., Zhang, J.J., Chakrabarti, C. et al. Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing. J Sign Process Syst 65, 211–227 (2011). https://doi.org/10.1007/s11265-011-0601-2

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  • DOI: https://doi.org/10.1007/s11265-011-0601-2

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