Abstract
Aiming at the effective utilization of multi-sensor measurement in measurement uncertainty, a multi-sensor adaptive particle filter algorithm is proposed. In the algorithm, multi-sensor measurement set is sampled by the random sampling strategy and measurement model transition probability. Then state estimation and the update of multi-sensor measurement set are realized by re-sampling in particle filter. Finally, the current moment measurement is validated according to the proportion of measurement number of single sensor in multi-sensor measurement set after re-sampling. The adverse influence of interference to the computational complexity is by reasonably selecting effective measurement. The theoretical analysis and experimental results show the efficiency of the proposed algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 547-550+556 |
| Journal | Kongzhi yu Juece/Control and Decision |
| Volume | 27 |
| Issue number | 4 |
| State | Published - Apr 2012 |
Keywords
- Information fusion
- Measurement uncertainty
- Multi-sensor
- Nonlinear filter
- Particle filter