非线性状态方程自校准滤波方法

In view of the situation that system state equations are influenced by unknown inputs (such as environmental influence, improper selection of models or parameters, and etc. ), a nonlinear self-calibration filtering recursive method for state equations with unknown inputs was proposed based on two nonlinear Kalman filtering methods, the self-calibration rank filter (SRF) and the self-calibration unscented Kalman filter (SUKF) were discussed respectively. According to numerous numerical simulation results and engineering applications, by estimating and compensating the unknown inputs in state equations automatically, the proposed algorithm can improve the filtering effect of the system under the influence of unknown inputs, and the estimation accuracy increased by 80% when compared with the unscented Kalman filtering method (UKF). Moreover, the calculation was simple and convenient for engineering applications.