Space Objects Classification Techniques : A Survey

被引:0
|
作者
Jahirabadkar, Sunita [1 ]
Narsay, Prajakta [1 ]
Pharande, Shivani [1 ]
Deshpande, Gargi [1 ]
Kitture, Anusha [1 ]
机构
[1] Cummins Coll Engn Women, Dept Comp Engn, Pune, Maharashtra, India
关键词
Deep Neural Networks (DNN); Convolutional Neural Network (CNN); Light Curves; Long/Short Term Memory (LSTM); Spaces Debris;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Space debris is a collection of some natural meteoroids or manmade objects floating in space. The amount of space debris has risen tremendously over the last few years. Due to their high (8km per sec) velocity, these debris cause a major threat to active space missions. For surveillance of active satellites, Space Situational Awareness is one of the important fields to be studied. Hence, to protect active satellites, it is important to classify the space objects as debris and apply collision avoidance techniques. This paper presents a survey on various approaches being used for classification of space objects using light curves as a differentiating characteristic. Classification of space objects based on k-nearest neighbour algorithms and various Deep Learning algorithms such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN) is researched.
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页码:786 / 791
页数:6
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