Desensitized Kalman Filtering with Analytical Gain

Abstract

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.