Distributed Nonlinear Fusion Estimation Without Knowledge of Noise Statistical Information: A Robust Design Approach
Rusheng Wang, Bo Chen, Li Yu
Abstract
This article is concerned with the distributed fusion estimation problem for nonlinear systems without knowledge of noise statistical information. By using the Taylor expansion, the linearization errors are modeled by the state-dependent terms with uncertain parameters when constructing nonlinear estimation error systems. Then, based on the idea of bounded recursive optimization, a robust design approach is developed to obtain local nonlinear estimators and distributed fusion criterion such that the designed local/fusion estimators are stable. Notice that, by establishing different convex optimization problems, the gains of stable nonlinear local/fusion estimators can be directly obtained using the standard softwares. Finally, two illustrative examples are exploited to show the advantages and effectiveness of the proposed methods.