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Updated: Jun 4, 2025

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Real-Time Radar Classification Based on Software-Defined Radio Platforms: Enhancing Processing Speed and Accuracy

Seckin Oncu1, Mehmet Karakaya2, Yaser Dalveren3

  • 1TUBITAK BILGEM, Ankara 06100, Turkey.

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|December 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a real-time radar classification system using software-defined radio (SDR) and the DBSCAN algorithm. It achieves high processing speed and 89.7% accuracy for identifying threat radars in electronic support measures.

Keywords:
GPUclusteringelectronic support measuresparameter extractionradar classificationsoftware-defined radio

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Software-defined radio (SDR) has digitized radio systems, enhancing flexibility and reconfigurability.
  • Radar signal parameters, within pulse description words (PDWs), are crucial for electronic support measure (ESM) systems.
  • Classifying threat radars in real-time is vital for modern defense and surveillance.

Purpose of the Study:

  • To develop and evaluate an SDR-based system for real-time radar classification.
  • To enhance processing speed and accuracy in radar detection and identification.
  • To leverage advanced algorithms and hardware acceleration for improved performance.

Main Methods:

  • Utilized software-defined radio (SDR) platforms for real-time signal acquisition.
  • Implemented the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for classification.
  • Employed Graphical Processing Unit (GPU) parallelization for efficient radio frequency (RF) parameter extraction.

Main Results:

  • Achieved real-time radar classification with significantly enhanced processing speed.
  • Demonstrated high efficiency at sampling rates up to 200 MSps.
  • Obtained an accuracy of 89.7% for classifying threat radars.

Conclusions:

  • The proposed SDR-based system offers an effective solution for real-time radar classification.
  • GPU parallelization and DBSCAN significantly improve processing speed and classification accuracy.
  • This approach is highly suitable for electronic support measure (ESM) applications requiring rapid threat identification.