Desensitized Extended Kalman Filter with Stochastic Approach to Sensitivity Reduction and Adaptive Weights

The desensitized Kalman filter can robustly estimate the state of a system with uncertain parameters without knowledge about uncertainty type. In this paper, the desensitized Kalman filter for nonlinear systems is derived using Taylor series expansion and a stochastic approach to reduce estimation error sensitivity to uncertain parameters. Adaptively normalized weights tune the trade-off between the minimum uncertainty sensitivity and minimum mean square error. Among the main benefits of the algorithm are intuitive tuning concerning uncertainty and a form resembling the classical Riccati equation. The comparison to other robust state-of-the-art algorithms is discussed based on a numerical example.