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A Deep Learning-Based Satellite Target Recognition Method Using Radar Data.

Wang Lu1, Yasheng Zhang2, Can Xu3

  • 1Graduate School, Space Engineering University, Beijing 101416, China. wanglu199310@163.com.

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Summary
This summary is machine-generated.

This study introduces a new satellite recognition method using radar data partitioning and deep learning. Orbital altitude helps segment data, improving high-resolution range profile (HRRP) analysis for more accurate satellite identification.

Keywords:
deep learninggated recurrent unit (GRU)high resolution range profile (HRRP)radar automatic target recognition (RATR)radar data partition

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Area of Science:

  • Aerospace Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Accurate satellite target recognition is crucial for space situational awareness.
  • Traditional methods struggle with complex radar data and feature extraction.
  • Deep learning offers potential for enhanced pattern recognition in radar signals.

Purpose of the Study:

  • To develop a novel method for satellite target recognition using radar data.
  • To improve the accuracy and efficiency of recognizing satellites from radar observations.
  • To leverage deep learning for advanced feature extraction from high-resolution range profiles (HRRPs).

Main Methods:

  • Introduced orbital altitude as a feature for radar data partitioning.
  • Developed a new normalized angular distance divided by correlation coefficient (NADDCC) metric.
  • Applied hierarchical clustering for radar observation angular domain segmentation.
  • Designed and implemented a Gated Recurrent Unit (GRU)-Support Vector Machine (SVM) model for HRRP target recognition.

Main Results:

  • Successfully partitioned radar data into distinct HRRP clusters based on orbital altitude and NADDCC.
  • GRU neural network effectively extracted highly distinguishable features from HRRPs.
  • GRU-SVM model demonstrated superior performance compared to LSTM and conventional RNN for HRRP recognition.
  • The proposed method outperformed other common feature extraction techniques and methods without data partitioning.

Conclusions:

  • The proposed radar data partitioning and GRU-SVM model offer a robust and effective approach for satellite target recognition.
  • Orbital altitude and NADDCC are valuable for segmenting radar data, enhancing recognition accuracy.
  • Deep learning, specifically GRU networks, excels at extracting discriminative features from HRRPs for improved satellite identification.