Abstract
A particle estimation algorithm where the weight of the particle is related to angle between observation vectors is presented for nonliear system state. When the likelihood has a bimodal nature, this algorithm leads to more accurate state estimates than Sequential importance resampling (SIR), Auxiliary particle filter (APF), Regularized particle filter (RPF), and Gaussian particle filter (GPF).
| Original language | English |
|---|---|
| Pages (from-to) | 700-702 |
| Number of pages | 3 |
| Journal | Chinese Journal of Electronics |
| Volume | 18 |
| Issue number | 4 |
| Publication status | Published - Oct 2009 |
| Externally published | Yes |
Keywords
- Auxiliary particle filter (APF)
- Gaussian particle filter (GPF)
- Nonliear system
- Particle alter
- Regularized particle filter (RPF)
- Sequential importance resampling (SIR)
- State estimation