Abstract
This paper presents the real-time moving horizon estimation of a spacecraft’s attitude and sensor calibration parameters, applied to two space mission scenarios. In the first scenario, the attitude is estimated from three-axis magnetometer and gyroscope measurements. In the second scenario, a star tracker is used to jointly estimate the attitude and gyroscope calibration parameters. A moving horizon estimator determines the current states and parameters by solving a constrained numerical optimization problem, considering a finite sequence of current and past measurement data, an available dynamic model and state constraints. The objective function to be minimized is typically a tradeoff between minimizing measurement noise, process noise, and an initial cost. To solve this constrained optimization problem in real time, an efficient numerical solution method based on the iterative Gauss–Newton scheme has been implemented and specific measures are taken to speed up the calculations by exploiting the sparsity and band structure of matrices to be inverted. Numerical simulation is used to verify that the proposed method results in a faster convergence from large initialization errors and an increased accuracy on nonlinear systems with respect to extended Kalman filtering.
Published Version
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