Autonomous celestial navigation method for a deep-space probe based on angle-constraint aided celestial angle measurement

High-precision measurement is necessary for high autonomous navigation of deep-space probes. There are many kinds of astronomical measurement information and exist many geometric constraints between astronomical measurement information. Measurement error has different characteristics and meets the corresponding constraints. In this study, an autonomous celestial navigation method for deep-space probes based on angle-constraint assisted celestial angular navigation is proposed. The method takes the orbit dynamics model of the deep-space probe as the state model, and the azimuth between Mars, Phobos, and Phobos and the probe, as the measurement, to establish the measurement model. The angle-constraint model of the measurement is established, and then the genetic algorithm sequence Quadratic programming-mixed nonlinear programming method is used for nonlinear programming of angle constrained model. Cubature Kalman filter (CKF) is used to estimate the system state to reduce the random error because the system models are nonlinear, and the influence of astronomical measurement accuracy on navigation results is analyzed. Simulation results show that the proposed method can suppress the multi-source measurement error and achieve high-precision autonomous navigation of the deep-space detector.