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Related Experiment Videos

Jamming Recognition Based on Adaptive Feature-Focusing Convolutional Neural Network for Agile Cognitive Radar.

Jialei Liu1, Jiazhi Ma1, Longfei Shi1

  • 1College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
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This study introduces an Adaptive Feature-Focusing CNN (AFF-CNN) to improve radar jamming recognition despite agile waveform parameters. The AFF-CNN enhances feature extraction, enabling accurate identification of jamming signals even with rapid parameter changes.

Area of Science:

  • Radar Systems Engineering
  • Artificial Intelligence in Signal Processing
  • Cognitive Radar

Background:

  • Deep neural networks are crucial for radar jamming recognition in cognitive radar systems.
  • Radar waveform parameter agility, an anti-jamming technique, challenges conventional CNN-based jamming recognition by altering signal features.
  • This creates a trade-off between effective jamming recognition and anti-jamming agility.

Purpose of the Study:

  • To develop a novel deep learning approach for robust radar jamming recognition.
  • To overcome the limitations imposed by radar inter-pulse parameter agility on jamming identification.
  • To enhance the adaptability of jamming recognition systems to dynamic radar environments.

Main Methods:

  • Proposed an Adaptive Feature-Focusing CNN (AFF-CNN) incorporating a pre-trained AFF module.
Keywords:
1D-2D feature fusionadaptive feature-focusing (AFF)feature scalesjamming recognitionmapping relationshipradar waveform parameter agility

Related Experiment Videos

  • The AFF module maps agile radar parameters to adaptive feature scales, calibrating deviations caused by parameter agility.
  • Utilized time-domain high-resolution range profiles (HRRP) and time-frequency domain short-time Fourier transform (STFT) data with a lightweight 1D-2D feature fusion CNN for single-pulse signal recognition.

    • Main Results:

    • The AFF-CNN demonstrated superior recognition accuracy compared to five other approaches.
    • The proposed method showed enhanced generalization capability in adaptive scenarios.
    • Effective adaptation to inter-pulse agility in radar systems was confirmed through simulations.

    Conclusions:

    • The AFF-CNN effectively addresses the challenge of radar parameter agility in jamming recognition.
    • The adaptive feature-focusing mechanism significantly improves the network's performance in dynamic environments.
    • This approach offers a promising solution for robust jamming recognition in advanced cognitive radar systems.