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A Deep-Learning Method for Radar Micro-Doppler Spectrogram Restoration.

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Summary
This summary is machine-generated.

This study introduces a deep learning method to remove radio frequency interference from radar spectrograms for improved human activity recognition (HAR). The approach effectively restores spectrogram quality, enhancing HAR system performance.

Keywords:
fully convolutional networkgenerative adversarial networkimage restorationradar micro-doppler spectrogram

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Area of Science:

  • Radar Signal Processing
  • Machine Learning for Signal Analysis
  • Human Activity Recognition

Background:

  • Radio frequency interference (RFI) significantly degrades radar spectrogram quality.
  • High-quality spectrograms are crucial for accurate micro-Doppler-based human activity recognition (HAR).
  • Existing methods struggle to effectively mitigate RFI in radar spectrograms.

Purpose of the Study:

  • To develop a deep learning-based method for detecting, removing, and restoring RFI in radar spectrograms.
  • To enhance the quality of radar spectrograms for improved HAR.
  • To validate the proposed method using both simulated and real-world radar data.

Main Methods:

  • A fully convolutional neural network (FCN) was utilized for RFI detection and removal.
  • A coarse-to-fine generative adversarial network (GAN) was proposed for spectrogram restoration in interference-affected regions.
  • The method was evaluated using simulated motion capture (MOCAP) and measured radar spectrograms.

Main Results:

  • The proposed deep learning method successfully detected and removed RFI from radar spectrograms.
  • Spectrograms were effectively restored in the regions affected by interference.
  • Both qualitative and quantitative experiments confirmed the mitigation of RFI and restoration of high-quality spectrograms.
  • Comparison experiments demonstrated the efficiency of the proposed approach.

Conclusions:

  • The developed deep learning technique offers an effective solution for RFI mitigation in radar spectrograms.
  • The method significantly improves spectrogram quality, thereby enhancing the potential for accurate micro-Doppler-based HAR.
  • The proposed FCN-GAN approach presents a promising advancement in radar signal processing for HAR applications.