Adaptive divided difference filter for nonlinear systems with unknown noise

Abstract

An Adaptive Divided Difference filter has been proposed for joint estimation of parameter and states of nonlinear systems in situations with unknown process noise statistics. The proposed filter, which is based on the innovation sequence, ensures improved estimation performance adapting the unknown process noise covariance. The performance of the filter is assessed with a benchmark nonlinear problem. Simulation results demonstrate that the performance of the proposed filter is superior compared to a non adaptive Divided Difference filter when the process noise covariance is unknown.