  A robust desensitized cubature Kalman filtering (DCKF) for nonlinear systems
with uncertain parameter is proposed. Sensitivity matrices are defined as the
integral form, and desensitized cost function is designed by penalizing the
posterior covariance trace by a sensitivity-weighting sum of the posteriori
sensitivities. The DCKF gain is obtained by minimizing the desensitized cost
function to amend the state estimation. Then, the sensitivity propagation of
the state estimate errors is described, and the sensitivity of the root square
matrix is obtained by solving a special equation. The effectiveness of the
proposed DCKF was demonstrated by two numerical simulations with uncertain
parameters.
