Sequential processing and performance optimization in nonlinear state estimation
We propose a framework for designing observers for noisy nonlinear systems
with global convergence properties and performing robustness and noise sensitivity. Our state
observer is the result of the combination of a state norm estimator with a bank of Kalman-type
lters, parametrized by the state norm estimator. The state estimate is sequentially processed
through the bank of lters. In general, existing nonlinear state observers are responsible for
estimation errors which are sensitive to model uncertainties and measurement noise, depending
on the initial state conditions. Each Kalman-type lter of the bank contributes to improve the
estimation error performances to a certain degree in terms of sensitivity with respect to noise
and initial state conditions. A sequential processing algorithm for performance optimization is
given and simulations show the eectiveness of these sequential lters.