Study of Steady-State Kalman Filtering Based on Thresholding Optimal Gain

摘要

For the devices with limited computing capacity and processing time, this paper proposes a new steady-state kalman filtering method based on thresholding optimal gain. The core algorithm is about improving the updating stage of the kalman filtering with a double-threshold optimal gain, which improves the dynamic tracking performance of the system steady-state while reducing the filtering algorithm computation. The results of the simulation prove that the algorithm has a better steady-state convergence effect than traditional kalman filtering algorithm. The method proposed in this paper can be widely used in many occasions, as it simplifies the computation of the optimal kalman gain and the a priori estimation.