On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Architectures for efficient implementation of particle filters
Architectures for efficient implementation of particle filters
Algorithmic and Architectural Design Methodology for Particle Filters in Hardware
ICCD '05 Proceedings of the 2005 International Conference on Computer Design
Resampling algorithms for particle filters: a computational complexity perspective
EURASIP Journal on Applied Signal Processing
Easy-hardware-implementation MMPF for Maneuvering Target Tracking: Algorithm and Architecture
Journal of Signal Processing Systems
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Dynamic Configuration of Time-Varying Waveforms for Agile Sensing and Tracking in Clutter
IEEE Transactions on Signal Processing - Part I
Resampling algorithms and architectures for distributed particle filters
IEEE Transactions on Signal Processing
Algorithmic and Architectural Optimizations for Computationally Efficient Particle Filtering
IEEE Transactions on Image Processing
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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.