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Badura.pdf
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🔗 Source: amostech.com
📊 Size: 17.65 MB
📄 Pages: 23 pages
⬇️ Downloads: 97

Summary

Convolutional neural networks (CNNs) are used to infer the attitude status of resident space objects (RSOs) from simulated light curve measurements. A synthetic dataset of 7500 light curves was generated, taking into account realistic bidirectional reflectance distribution function (BRDF) signatures and environmental parameters. The CNN achieved an overall classification accuracy of 86.2% across four maneuver classes: tumbling, accelerating, stabilizing, and inactive. However, the model performed worst when classifying inactive RSOs, with a 60% accuracy, due to complications such as back-scatter signatures and low signal-to-noise ratio. The results suggest that CNNs have potential for aiding in the problem of classifying satellite attitude status from light curves, but require further development of training sets and pre-processing techniques to account for these complications.

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Description

Convolutional neural networks (CNNs) are used to infer the attitude status of resident space objects (RSOs) from simulated light curve measurements.

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  • File Format: PDF
  • File Size: 17.65 MB
  • Pages: 23
  • Language: EN
  • Total Downloads: 97
  • Last Updated: 3 weeks ago

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