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Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle.

Jae-In Lee1, Nammon Kim2, Sawon Min2

  • 1Interdisciplinary Major of Maritime AI Convergence, Korea Maritime & Ocean University, Busan 49112, Korea.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary

Classifying space targets from debris is improved by a new method using micro-Doppler signatures that account for radar incident angles. This approach enhances classification accuracy, especially for distinguishing between space targets and tumbling debris.

Keywords:
classificationconvolution neural networkdebrisdeep learningincident anglemicro-Dopplerspace targets

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Area of Science:

  • Radar signal processing
  • Space surveillance and tracking
  • Machine learning for defense

Background:

  • Accurate classification of space objects is crucial for radar resource management and mid-course defense.
  • Deep learning methods utilizing micro-Doppler signatures have shown promise for space target classification.
  • Existing methods primarily use conventional micro-Doppler signatures like spectrograms and cadence velocity diagrams (CVD).

Purpose of the Study:

  • To propose a novel method for generating micro-Doppler signatures that incorporate the relative incident angle during radar tracking.
  • To evaluate the effectiveness of these new signatures in classifying space targets and debris using deep learning models.
  • To compare the performance of the proposed signatures against conventional ones.

Main Methods:

  • Generated novel micro-Doppler signatures (spectrogram and CVD) considering radar relative incident angles.
  • Employed transfer learning with AlexNet and ResNet-18 convolutional neural network architectures.
  • Trained and tested models on datasets classifying six types of space targets and debris, and later on a two-class problem (precessing targets vs. tumbling debris).

Main Results:

  • The proposed CVD signature achieved a classification accuracy of 92.11%, an improvement over the conventional CVD's 88.97%.
  • The proposed spectrogram signature showed lower accuracy than the conventional spectrogram.
  • For a two-class problem (space targets vs. debris), both proposed spectrogram and CVD achieved over 99.82% accuracy, with CVD outperforming spectrogram.

Conclusions:

  • Incorporating relative incident angles into micro-Doppler signature generation enhances space target and debris classification accuracy.
  • The proposed CVD signature is particularly effective, outperforming conventional methods and spectrograms, especially in distinguishing between precessing space targets and tumbling debris.
  • Deep learning models like AlexNet and ResNet-18 effectively leverage these enhanced signatures for high-accuracy classification.