Performance Optimization via Sequential Processing for Nonlinear State Estimation of Noisy Systems

We propose a framework for designing observers noisy nonlinear systems with global convergence properties and performing robustness noise sensitivity. This comes out from the combination of state norm estimator chain filters, adaptively tuned by estimator. The estimate is sequentially processed through filters. Each filter contributes to improving, certain amount, estimation error performances previous in terms sensitivity, this amount quantitatively evaluated using comparison criterion, which considers ratio asymptotic bounds two consecutive filters chain. A recursive algorithm given implementing guaranteeing sequential performance optimization process. Simulations show effectiveness these chains