Abstract: In many engineering fields,such as deep space exploration,navigation,fault diagnosis and so on,due to the influence of environmental factors, improper selection of models and parameters, the system state equation often contains unknown inputs (systematical errors). Traditional Kalman filters cannot eliminate the influence of unknown inputs, resulting in larger filtering errors. In this paper,a self-recognition and self-calibration Kalman filtering method is proposed. The linear and nonlinear systems are discussed, and the corresponding formulas and filtering steps are given. This method can automatically recognize whether there are unknown inputs in the state equation. When there are unknown inputs, they can beautomatically estimated, compensated and corrected them. A large number of examples and simulation results show that compared with the traditional method,the proposed method can effectively improve the accuracy of state estimations,and the calculation is simple, which is convenient for engineering application.