Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing |
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Authors: | Lifeng Miao Jun Jason Zhang Chaitali Chakrabarti Antonia Papandreou-Suppappola |
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Affiliation: | (1) School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA |
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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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