Comparative evaluation of three machine learning algorithms on improving orbit prediction accuracy

查看全文 作　　者：Hao [1]Peng;Xiaoli [1]Bai

机构地区：[1]Department of Mechanical and Aerospace Engineering,Rutgers,The State University of New Jersey,Piscataway,NJ 08854,USA

出　　处：《Astrodynamics》索引2019年第3卷第4期,共19页

基　　金：The authors would acknowledge the research support from the Air Force Office of Scientific Research(AFOSR)FA9550-16-1-0184 and the Office of Naval Research(ONR)N00014-16-1-2729.Large amount of simulations of RSOs have been supported by the HPC cluster in School of Engineering,Rutgers University. 摘　　要：In this paper,the recently developed machine learning(ML)approach to improve orbit prediction accuracy is systematically investigated using three ML algorithms,including support vector machine(SVM),artificial neural network(ANN),and Gaussian processes(GPs).In a simulation environment consisting of orbit propagation,measurement,estimation,and prediction processes,totally 12 resident space objects(RSOs)in solar-synchronous orbit(SSO),low Earth orbit(LEO),and medium Earth orbit(MEO)are simulated to compare the performance of three ML algorithms.The results in this paper show that ANN usually has the best approximation capability but is easiest to overfit data;SVM is the least likely to overfit but the performance usually cannot surpass ANN and GPs.Additionally,the ML approach with all the three algorithms is observed to be robust with respect to the measurement noise. 关键词：resident space objects(RSOs) orbit prediction machine learning(ML) support vector regression artificial neural network(ANN) Gaussian processes(GPs)

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Comparative evaluation of three machine learning algorithms on improving orbit prediction accuracy

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