Abstract
Based on rank filter (RF) and particle filter (PF), a rank particle filter (RPF) was presented. This method included the importance density function obtained from RF and the application of PF. Because of importance density function from RF containing the latest measurement information, its importance density function was more in line with the probability density of the truth state. So RPF has the higher accuracy than those of unscented particle filter (UPF) and PF, and it has been proved by lots of simulations. Furthermore, RPF is simple to calculate and easy to apply in engineering.
| Original language | English |
|---|---|
| Pages (from-to) | 894-898 |
| Number of pages | 5 |
| Journal | Jixie Qiangdu/Journal of Mechanical Strength |
| Volume | 36 |
| Issue number | 6 |
| State | Published - 15 Dec 2014 |
Keywords
- Kalman filter
- Nonlinear filter
- Particle filter
- Rank filter
- Rank particle filter
- Unscented particle filter
Fingerprint
Dive into the research topics of 'Rank particle filter'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver