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Related Experiment Video

Updated: Jun 4, 2025

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Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal Environments.

Ji-Hyeon Kim¹, Soon-Young Kwon¹, Hyoung-Nam Kim¹

¹Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea.

Sensors (Basel, Switzerland)

|December 17, 2024

Summary

This study introduces a new two-stage radar signal classification method using amplitude pattern analysis and visibility graphs. It effectively distinguishes overlapping signals, even in challenging low signal-to-noise ratio environments.

Keywords:

amplitude pattern radar scan pattern classification visibility graph

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Area of Science:

Signal Processing
Electronic Warfare
Machine Learning

Background:

Classifying overlapping radar signals is difficult in complex environments.
Traditional methods like spectrogram analysis fail with similar scan patterns or low signal-to-noise ratios (SNR).

Purpose of the Study:

To develop a novel framework for accurate and efficient radar signal classification and deinterleaving.
To overcome limitations of traditional methods in complex electronic warfare scenarios.

Main Methods:

A two-stage classification framework combining amplitude pattern (AMP) analysis and visibility graphs.
AMP analysis for initial signal grouping and noise reduction.
Visibility graphs for refining classifications and separating similar amplitude signals.
Integration of deep learning models (GoogLeNet, ResNet) for enhanced performance.

Main Results:

The framework effectively classifies and deinterleaves overlapping radar signals.
Demonstrated robustness in low-SNR and multi-signal environments.
Successfully handled complex scans, including the Palmer series.

Conclusions:

The proposed method significantly improves radar signal differentiation over conventional techniques.
Offers enhanced performance for various scanning patterns in challenging multi-signal environments.