Classification for geosynchronous satellites with deep learning and multiple kernel learning.

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Abstract

A detailed understanding of space objects is one of the important goals of space situational awareness. The geostationary orbit (GEO) belt is an important space asset for human beings, so the identification of GEO satellites is one of the measures to ensure the safety of GEO objects (GEOs). In this paper, we propose using deep learning based on recurrent neural networks (RNNs) and convolutional neural networks (CNNs), and multiple Kernel learning (MKL) to identify the shape and attitude of GEOs synchronously via light curves. Our algorithm focuses mainly on optical data obtained from the real measured data collected by optical laboratory and computer simulation. We first acquired light curves of five GEO satellites for 1 year; then, we constructed a network architecture consisting of CNNs and RNNs to automatically extract the different scale characteristics of the collected light curves of GEOs. Next, we use the MKL to fuse the extracted features of different scales. Finally, the support vector machine is used to provide the classification and recognition results of the shape and attitude of five GEOs. The network architecture proposed is compared with more conventional machine learning techniques (e.g., principal component analysis, linear discriminant analysis) and is shown to outperform such methods. At the same time, the classification effect of the multiple kernel is better than the single kernel in this experiment.