| Autor: |
Gargi Deshpande, Prajakta Narsay, Shivani Pharande, Sunita Jahirabadkar, Anusha Kitture |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 International Conference on Computational Performance Evaluation (ComPE). |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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