Abstract
The state estimation for multi-sensor nonlinear systems with model parameters uncertainty and correlated noise is considered. Firstly, a new cubature smooth variable structure filter with model uncertainty and correlation noise is proposed. Secondly, a general cross-covariance framework of any local estimators is derived for multi-sensor systems with correlated noise, and the nonlinear integral is calculated by Cubature rule. Then, based on matrix weighting, the distributed Cubature smooth variable structure fusion algorithm is proposed for multi-sensor nonlinear systems with model parameters uncertainty and correlation noise. Finally, the simulation results show that the proposed algorithms can effectively overcome the interference of model parameters uncertainties and correlation noise, and also have higher estimation accuracy.
| Translated title of the contribution | Distributed cubature smooth variable structure fusion algorithm with model uncertainties and cross-correlation noise |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 1999-2009 |
| Number of pages | 11 |
| Journal | Kongzhi Lilun Yu Yinyong/Control Theory and Applications |
| Volume | 42 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - 2025 |
| Externally published | Yes |