针对飞行器在卫星拒止环境下的高精度、高完备等导航定位需求，提出了一种融合捷联惯导、卫星导航和气压高度计的多源自主导航鲁棒滤波方法。在量测中断后较短的时间内，该方法通过量测不确定性与非线性误差模型的滤波器估计，能够精确量化紧组合导航模式中的状态空间模型，在深入分析量测异常向量对滤波器状态输出的作用机理基础上，引入鲁棒容积卡尔曼滤波设计，有效改善了误差协方差矩阵的抑制效果，提升了多源自主导航解译过程中滤波器的稳定性和状态方程的估计精度。仿真结果表明：典型飞行运动模式下，该方法相对于传统的容积卡尔曼滤波，陀螺仪与加速度计的零偏状态估计精度提升约31%，自主导航系统定位精度提升约23.77%，并更好地抑制了姿态角误差，为国家综合PNT体系多源自主导航终端的可靠应用提供了参考借鉴。

To meet the requirements for 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 tightly integrated navigation model through the filter estimation of the measurement uncertainty and nonlinear error model. Based on an in-depth analysis of the mechanism of the measurement anomaly vector’s action on the filter state output， a robust volume Kalman filter is proposed. 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 method proposed can improve the accuracy of zero bias estimation of the gyroscope and accelerometer by about 31% and the positioning accuracy of autonomous navigation system by about 23.77%， and suppress the attitude angle error. This study can provide a reference for the terminal application of multi-source autonomous navigation system of the new generation of national integrated PNT system.