Satellite Attitude Change Recognition Based on Multi-Frame Image by 3D Convolutional Neural Networks

Abstract

The recognition of satellite's attitude change plays an important role in the detection, tracking and recognition of space targets, as well as the evaluation, verification of space events and environmental monitoring and prediction. In this paper, 3D-CNN model is used to extract features from spatial and temporal dimensions, and then 3D convolution is carried out to capture motion information from multiple consecutive frames. Four common attitude changes of three different kinds of satellites are simulated, which are orbit change, spin, reconnaissance and maneuver. A proper number of consecutive frames are sent into packets and sent to the network for training. The experimental result shows that 3D-CNN model has a competitive performance.