Abstract
Autonomous celestial navigation has been exploited for orbit determination of deep space exploration. Geometric constraints in celestial measurement are the inherent attributes in actual comprehensive autonomous celestial navigation physical systems; they are usually neglected in autonomous navigation systems which causes a loss of information in measurement. For the purpose of high-precision autonomous celestial navigation, the geometric constraints should be utilized as fully as possible. This paper proposes a geometric coplanar constraint for the mutually dependent celestial measurement (line of sight or vectors), and the geometric coplanar constraint model is established. The sequence quadratic program (SQP) algorithm based on the geometric coplanar constraint is put forward to eliminate the dependence of multiple celestial measurements, and suppress the noises in celestial measurement geometrically. Taking both geometry coplanar constraints of celestial measurement and the nonlinear characteristics of system models into account, cubature Kalman filter with measurement optimization is proposed for decreasing the random noise in measurements geometrically and statistically. Simulations demonstrate that the proposed geometric coplanar constraints-aided autonomous celestial navigation method can effectively eliminate the measurement noise geometrically and statistically, and achieve high-precision performance.
Highlights
Deep space exploration is an important area of the world space activities
The existing state estimation methods used in autonomous navigation of deep space exploration include the extended Kalman filter (EKF), sigma-point Kalman filter (SPKF), and particle filter (PF) [8], [16]
To improve the navigation accuracy of the autonomous celestial navigation of a deep space explorer, it is necessary to model the constraints in measurement of the autonomous celestial navigation system, which can directly eliminate the effects of measurement error with the geometric constraints, and achieve high precision by using measurement geometric constraints
Summary
Deep space exploration is an important area of the world space activities. At the beginning of the 21st century, the aerospace powers have focused their attention on the deep space beyond 380,000 kilometers from the Earth. The existing state estimation methods used in autonomous navigation of deep space exploration include the extended Kalman filter (EKF), sigma-point Kalman filter (SPKF), and particle filter (PF) [8], [16]. The geometric constraints have not been considered in the studies of the autonomous celestial navigation of a deep space explorer. To improve the navigation accuracy of the autonomous celestial navigation of a deep space explorer, it is necessary to model the constraints in measurement of the autonomous celestial navigation system, which can directly eliminate the effects of measurement error with the geometric constraints, and achieve high precision by using measurement geometric constraints.
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