A Strong Tracking and Robust Kalman Filter

In practical applications, only the output noise, but not the disturbance of the system, can be measured directly. In this case, the effectiveness of the designed Kalman filter is poor and even diverges because of the disturbance. In this paper, we make use of the improved Sage-Husa state disturbance statistical estimators to estimate the mean and variance of the system state disturbance in real time. To provide more robustness, the strong tracking Kalman filter algorithm is introduced to improve the variance of state prediction in time. For the velocity model of the gyro-stabilized platform with different positive and negative velocity model parameters, we verify the superiority and practicability of the algorithm proposed through comparative experiments under different system conditions in the simulation experiments. This paper designs a better Kalman filter with process disturbances and noise and provides a deep study and investigation by means of a comparative analysis of performance.