Adaptive Fast Desensitized Kalman Filter

Adjusting the sensitivity-weighting matrix, which is a key parameter affecting the filtering accuracy in the desensitized Kalman filter (DKF), is still an open problem. To address this issue, a new adaptive fast DKF (AFDKF) algorithm and adaptive fast desensitized extended Kalman filter (AFDEKF) have been proposed. The fast filters have an adaptive factor that enables them to adjust the sensitivity-weighting matrix based on the orthogonality principle of measurement residuals. This adaptive factor is calculated by using the corresponding process and measurement information. Then, a new desensitized cost function with an adaptive factor is designed. An analytical gain is obtained by minimizing this cost function to reduce computation cost. The performance of the AFDKF and AFDEKF algorithms are demonstrated using two numerical examples.