The invention discloses a polarized light inertia rigorous integrated navigation method based on self-learning multi-rate residual correction. When a polarized light system works normally, a course angle output by an inertial navigation system is converted into an azimuth angle of a carrier body axis relative to the solar meridian and serves as a state quantity, the azimuth angle, calculated by the polarized light navigation system, of a carrier body axis relative to the solar meridian serves as an observed quantity, and CKF-ERC is used for fusing two data; the time information and the z-axisangular rate are taken as input of a long-short-term memory neural network, data resolved by a polarized light navigation system is taken as network output, and the network output is trained to realize a self-learning function; and when the polarized light system is not available, an error compensation stage is started, the data of the polarized light is predicted through LSTM, and data fusion isperformed on the predicted value and the azimuth angle calculated by inertia, thereby improving the long-time navigation precision of a downloading body in a complex environment. The autonomy of navigation orientation can be effectively improved, and then the intelligent degree of an unmanned aerial vehicle is improved.
Polarized light inertia rigorous integrated navigation method based on self-learning multi-rate residual correction