Incremental Kalman Estimation Algorithm Analysis
Xinhe Wang, Mandi Wang, Xiaojun Sun · 2019
Applying the projective theory, the incremental Kalman estimation algorithms including filter, predictor and smoother are presented based on the optimal estimation criterion of linear minimum variance. They solve the state estimation problem for the systems with unknown measurement errors, which don't meet the requirements of classical Kalman filter. The proposed algorithms have the advantages of simple form and small computational burden so as to be easily used in the engineering practice. The simulation results show their effectiveness and feasibility.