Abstract: Aiming at the requirements of high precision and high completeness in navigation and positioning of spacecraft in the environment of satellite rejection, a robust filtering method for multi-source autonomous navigation is proposed, which combines strapdown inertial navigation, satellite navigation and barometric altimeter. In a short period of time after measurement interruption, this method can accurately quantify the state space model in the compact integrated navigation model through the filter estimation of the measurement uncertainty and nonlinear error model. Based on the in-depth analysis of the action mechanism of the measurement anomaly vector on the filter state output, a robust volume Kalman filter design is introduced. The suppression effect of error covariance matrix is effectively improved, and the stability of filter and the estimation accuracy of state equation are improved in the process of multi-source autonomous navigation interpretation. The simulation results show that compared with the traditional volume Kalman filter, the zero bias accuracy of gyroscope and accelerometer is improved by about 31%, the positioning accuracy of autonomous navigation system is improved by about 23.77%, the attitude Angle error is suppressed to a certain extent, which provides a reference for the terminal application of multi-source autonomous navigation system under the new generation of national integrated PNT system.

Key words: multi-source autonomous navigation, GNSS denied, nonlinearity robust filtering, tightly integration