Robust Parallel Covariance Intersection Fusion Estimators for Uncertain AR signal Systems
Xue Liu, Zhibo Yang · 2023
This paper is concerned with robust parallel covariance intersection fusion estimation problem for AR systems with uncertain noise variances. By the state space method, the original system is converted into a multi-model system. According to the mini-max robust estimation principle, the local robust estimators are presented. And the PCI fusion estimators are presented according to the PCI fusion algorithm. The robustness and the robust accuracies of them are proved. A simulation verifies the correctness and effectiveness of the proposed results.