Abstract
In order to improve the navigation accuracy of long-distance cruise missiles, a rapid and high precise calibration for a strap-down inertial navigation system (SINS) by using the integration of neural networks (NN) and star sensors was presented in this paper. It was implemented by two parts: firstly, for the sake of compensating the certain errors, a new method of NN input–output samples structure was presented to train the NN for automatically and fast calibrating the cruise missile. When the cruise missile was appended under the wing, the trained NN can be directly used for automatic calibration under the free-flight phase. Secondly, in order to ulteriorly correct the navigation errors accumulated by random noises of gyroscopes and accelerometers, the high precision attitude information of star sensors were used to correct the attitude matrix of SINS termly, and to reach the goal of decreasing the navigation error induced by random noise. In the end, the simulation results indicate that this method is feasible.
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