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Utilizing Polarization Diversity in GBSAR Data-Based Object Classification.

Filip Turčinović1, Marin Kačan1, Dario Bojanjac1

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Summary
This summary is machine-generated.

This study enhances object classification using Ground-Based Synthetic Aperture Radar (GBSAR) by integrating polarization data. Simple data concatenation with deep learning models proved most effective for improving radar sensing performance.

Keywords:
ResNet18ground-based SARobject classificationpolarizationradar data

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Area of Science:

  • Microwave and Millimeter Wave Sensing
  • Intelligent Sensor Systems
  • Deep Learning Applications

Background:

  • Advancements in affordable hardware have spurred growth in microwave and millimeter wave sensing.
  • Previous work explored object classification using raw radar data from the GBSAR-Pi system.

Purpose of the Study:

  • To analyze the potential of polarization information for improving deep learning models using raw Ground-Based Synthetic Aperture Radar (GBSAR) data.
  • To investigate different strategies for integrating dual-polarization radar data into classification models.

Main Methods:

  • Acquisition of GBSAR data at 24 GHz with both vertical (VV) and horizontal (HH) polarization.
  • Development of classification models using a modified ResNet18 architecture.
  • Introduction of a novel Siamese architecture designed for dual-input radar data.

Main Results:

  • Integration of VV and HH polarization data significantly impacts deep learning model performance.
  • A simple data concatenation method emerged as the most effective approach for combining polarization information.
  • The study highlights the critical role of antenna polarization and data merging strategies.

Conclusions:

  • Utilizing polarization information is crucial for enhancing deep learning-based object classification with GBSAR data.
  • The choice of merging strategy for dual-polarization data directly influences classification accuracy.
  • Future research should focus on optimizing polarization data integration in radar sensing applications.