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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.

Sensors (Basel, Switzerland)

|December 17, 2024

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:

GPU clustering electronic support measures parameter extraction radar classification software-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.