Name
Abstract | ||
|---|---|---|
Resampling is employed to alleviate the phenomenon of particle degradation in a particle filter. However, resampling weakens the diversity of particles, and results in inadequate convergence accuracy. To coordinate the conflict between the validity and diversity of the particle sets, an adaptive multi-level resampling based particle filtering algorithm (ARPF) is proposed in this paper. The particle resampling weight space algorithm is divided into multiple levels, and the diversity of the resampled particles is measured by the rank entropy to conduct resampling. The experimental results show that the multi-level resampling particle filtering algorithm can improve the accuracy, and is suitable for high precision system filtering. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CSE-EUC.2017.60 | 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) |
Keywords | Field | DocType |
particle filtering,resampling,diversity measurement,rank entropy | Convergence (routing),Mathematical optimization,Computer science,Particle filter,Algorithm,Filter (signal processing),Particle filtering algorithm,Weight space,Resampling,Auxiliary particle filter,Particle,Distributed computing | Conference |
Volume | ISSN | ISBN |
1 | 1949-0828 | 978-1-5386-3222-2 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| yan zhang | 1 | 67 | 20.55 |
| Zhaobin Liu | 2 | 88 | 23.03 |
| Bin Zhang | 3 | 60 | 40.23 |
| Fahong Yu | 4 | 17 | 5.17 |