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A robust desensitized cubature Kalman filtering (DCKF) for nonlinear systemswith uncertain parameter is proposed. Sensitivity matrices are defined as theintegral form, and desensitized cost function is designed by penalizing theposterior covariance trace by a sensitivity-weighting sum of the posteriorisensitivities. The DCKF gain is obtained by minimizing the desensitized costfunction to amend the state estimation. Then, the sensitivity propagation ofthe state estimate errors is described, and the sensitivity of the root squarematrix is obtained by solving a special equation. The effectiveness of theproposed DCKF was demonstrated by two numerical simulations with uncertainparameters.

Desensitized Cubature Kalman Filter with Uncertain Parameter