A Nonlinear Self-recognition Self-calibration Kalman Filtering Method - FU Huimin; YANG Haifeng; WEN Xinlei (Kongzhi Yu Xinxi Jishu)
In view of the strong nonlinearity of state equations and the influence of unknown inputs (systematic errors) in deep space exploration, guidance and control, and fault diagnosis, a nonlinear self-recognition self-calibration filtering method was proposed. According to the rank sampling and Sigma points sampling respectively, the rank sampling self-recognition self-calibration Kalman filter and the Sigma points sampling self-recognition self-calibration Kalman filter were discussed in detail. Firstly, the proposed method can automatically identify unknown inputs in nonlinear state equations, and then estimate and compensate their influence when there are unknown inputs, which can not only effectively eliminate the influence of systematic errors in state equations, but also reduce the random errors through the fusion of state equations and measurement equations, improving the filtering accuracy. It can be seen from the examples that the estimation accuracy is at least 64% higher than that of the unscented Kalman filter and the adaptive unscented Kalman filter.