Name
Title | ||
|---|---|---|
Algorithm and Parallel Implementation of Particle Filtering and its Use in Waveform-Agile Sensing |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s11265-011-0601-2 | Journal of Signal Processing Systems |
Keywords | Field | DocType |
Particle filter,Waveform-agile sensing,Parallel architecture,Field programmable gate array,Target tracking | Nonlinear system,Computer science,Waveform,Particle filter,Parallel computing,Field-programmable gate array,Algorithm,Mean squared error,Real-time computing,Resampling,Recursion,Performance improvement | Journal |
Volume | Issue | ISSN |
65 | 2 | 1939-8018 |
Citations | PageRank | References |
7 | 0.55 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lifeng Miao | 1 | 23 | 3.43 |
| Jun Jason Zhang | 2 | 122 | 18.78 |
| Chaitali Chakrabarti | 3 | 1978 | 184.17 |
| Antonia Papandreou-Suppappola | 4 | 234 | 29.88 |