Star sensor installation error calibration in stellar-inertial navigation system with a regularized backpropagation neural network

Abstract

The star sensor is the attitude reference in a stellar-inertial navigation system. It is essential to acquire the star sensor installation error, which has a great influence on the system navigation performance. However, traditional methods have a poor tolerance for a large range of installation errors, especially when the system works under a separate installation mode. In this paper a novel calibration method, using a regularized backpropagation (BP) neural network, is proposed. With a specially designed calibration procedure, the neural network is structured with BP and the regularization is improved. The network training is conducted for parameter solidification. The calibration can be achieved without formula derivation and numerical calculation under both small and large installation errors. In the experiment, the calibration accuracy is about 5 arcsec under small installation errors and about 20 arcsec under large installation errors, which is much better than a Kalman filter. The proposed method has the potential to be a universal star sensor calibration method under integrative installation mode or separated installation mode with large installation error.