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Related Experiment Video

Updated: Jan 9, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

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Learning transformer network for effective radar chaff jamming suppression.

Shuolei Li¹, Junli Zhu¹, Jingping Liu²

¹School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

Scientific Reports

|November 29, 2025

Summary

This study introduces a transformer network to suppress radar chaff jamming, significantly improving signal quality. The novel approach enhances radar

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Area of Science:

Radar Systems Engineering
Signal Processing
Artificial Intelligence

Background:

Chaff jamming poses a significant challenge to radar systems.
Effective suppression of chaff jamming is crucial for maintaining radar performance.

Purpose of the Study:

To propose an effective transformer network for suppressing radar chaff jamming.
To improve the anti-chaff jamming capabilities of radar systems.

Main Methods:

Utilizing a transformer network with a scaled dot-product multi-head self-attention mechanism.
Modeling non-local information for radar echo signal reconstruction.
Establishing a diverse dataset of measured chaff radar echo signals.

Main Results:

Achieved a mean square error of 0.0258.
Reached a correlation coefficient of 93.26%.
Improved signal-to-noise ratio by 11.1475 dB.

Conclusions:

The proposed transformer network effectively suppresses chaff jamming.
The method establishes a new benchmark for radar chaff jamming suppression.
The developed dataset is a valuable resource for future research.