Design of adaptive strong tracking and robust Kalman filter

Abstract

Only the output noise of system can often be measured in practical application. The disturbance of system state is generally unknown. In this case, the effectiveness of the Kalman filter designed is poor, can not be used, and even causes the divergency. What we do in the process of Kalman filter design is to estimate on-line the unknown disturbance of system state, make use of the improved Sage-Husa state disturbance statistical estimators to estimate the real-time mean and variance of the system state disturbance. In order to further ensure the algorithm robustness to the system disturbance, the strong tracking Kalman filter algorithm was introduced to correct the variance of state prediction in real time. In the application of the velocity model of gyro-stabilized platform with different positive and negative velocity model parameters, we verified the superiority and practicability of the algorithm through comparative experiments under different conditions of the system by the simulation experiments. This paper designs a better Kalman filter with state disturbances and noise, gives a deeply study and investigation by means of the comparative analysis of application performance.