Stubborn and Dead-Zone Redesign for Nonlinear Observers and Filters

We propose a redesign paradigm for stable estimators by introducing saturation or dead-zone nonlinearity with adaptive thresholds on the output injection term. Such nonlinearities allow improving sensitivity to measurement noise in different scenarios (impulsive disturbances persistent such as sensor bias), while preserving asymptotic convergence properties of original observer. These redesigns apply broad class state estimators, including linear observers, observers input-affine systems, Lipschitz based circle criterion, high-gain standard and extended Kalman filters. Simulation results confirm effectiveness both stubborn redesigns.