Abstract
Sampling-based filtering approaches are widely used for the on-line estimation problem of non-Gaussian nonlinear systems. They commonly take advantages of matching the prior density of system state by certain sampling strategies. Theoretical analysis and experimental results on several popular deterministic sampling filters were proposed to show performance on coping with non-gauss nonlinear filtering problems; moreover, guidelines and suggestions were given for practical appliance requisitions.
| Original language | English |
|---|---|
| Pages (from-to) | 4265-4269 |
| Number of pages | 5 |
| Journal | Xitong Fangzhen Xuebao / Journal of System Simulation |
| Volume | 19 |
| Issue number | 18 |
| State | Published - 20 Sep 2007 |
Keywords
- Central Difference Filter
- Deterministic sampling
- Divided Difference Filter
- Estimation
- Gaussian-Hermite Filter
- Nonlinear filter
- Unscented Kalman Filter
Fingerprint
Dive into the research topics of 'Comparison and analysis of deterministic sampling filters for state estimation'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver