In this paper, a new filter referred to as the sliding innovation filter (SIF) is presented. The SIF is an estimation strategy formulated as a predictor-corrector that makes use of a switching gain and innovation term. In estimation theory, a trade-off exists between robustness to disturbances and optimality in terms of estimation error. Unlike the Kalman filter (KF), the SIF is a sub-optimal filter in the sense that it does not provide the optimal solution to the linear estimation problem.

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... ver, the switching gain provides an inherent amount of robustness to estimation problems that may be ill-conditioned or contain modeling uncertainties and disturbances. The paper includes the proof of stability and explanation of the SIF gain. Furthermore, the SIF is extended to nonlinear estimation problems using a Jacobian matrix, resulting in the extended sliding innovation filter (ESIF). The methods are applied to a linear and nonlinear aerospace actuator system under the presence of a leakage fault. The results of the simulation demonstrate the improved performance of the SIF and ESIF strategies over popular KF-based methods.