Research on space object classificationbased on LSTM-Attention
Aiming at the problems of the difficulty of describing the unique attributes of space objects and their motion trends using a single element,as well as the low accuracy of the existing space object classification techniques,constructs a space object classification model based on LSTM-Attention.The model does not need to carry out additional feature engineering,and is capable of linking the contextual information and long-term dependencies of space object sequence data,extracting the local features of the object and modelling its long-term motion trends.Validated using real light curve observations taken from the Mini-Mega TORTORA(MMT)system,designed to use a model with high data processing efficiency compared to traditional methods,and is able to improve the classification accuracy of space objects and satisfy some of the application requirements of space situational awareness.
space object classificationdeep learningLSTMattention mechanism
NETL
NSTL
万方数据
