  The possible methodologies to handle the uncertain parameter are reviewed.
The core idea of the desensitized Kalman filter is introduced. A new cost
function consisting of a posterior covariance trace and trace of a weighted
norm of the state error sensitivities matrix is minimizing to obtain a
well-known analytical gain matrix, which is different from the gain of the
desensitized Kalman filter. The pre-estimated uncertain parameter covariance is
set as a referential sensitivity-weighting matrix in the new framework, and the
rationality and validity of the covariance are tested. Then, these results are
extended to the linear continuous system.
