| Citation: | LIAO Chuan, BAI Xue, XU Minget al. Correction of space atmospheric model based on data mining method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(12): 2628-2636. doi: 10.13700/j.bh.1001-5965.2018.0335(in Chinese)
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The empirical atmospheric model would cause great error in orbital prediction. This paper, taking a typical satellite as the benchmark spacecraft, proposes two orbital prediction models with different precision to generate training data and test data. Using three supervised classification methods in data mining technology, i.e. support vector machine (SVM), neural network (NN), and random forest (RF), to learn the errors caused by atmospheric model in orbital prediction. In this way, the deviation between the atmospheric model and its real value can be recovered and then corrected. Classification training results show that due to the randomness and voting mechanism, RF makes the highest accuracy in recovering the known deviation of atmospheric model close to 99.99% through choosing maximum probability, which is followed by SVM with the maximum accuracy of 50.7%. It is often the case that feedforward backpropagation neural network fails to learn, so the application performance is poor. Compared with traditional statistical methods, the method proposed in this paper has the advantages of rapidly processing big datasets and the ability of mining potential knowledge in tiny orbital prediction errors.
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