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Updated: Jun 29, 2026

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Radar UAV/Bird Trajectory Feature Classification Based on TCN-Transformer and the PC-TimeGAN Data Augmentation

Fei Tong1, Kun Zhang2, Guisheng Liao1

  • 1National Key Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

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Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

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This study introduces a novel method for identifying unmanned aerial vehicles (UAVs) using radar. By combining physics-constrained TimeGAN data augmentation with a TCN-Transformer model, it improves classification accuracy for low-altitude targets.

Area of Science:

  • Radar Signal Processing
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Low-altitude radar recognition faces challenges with limited unmanned aerial vehicle (UAV) track samples and similar motion patterns between UAVs and birds.
  • Severe class imbalance further complicates accurate UAV detection and classification.

Purpose of the Study:

  • To develop an advanced trajectory classification method for low-altitude UAVs.
  • To address data scarcity, class imbalance, and motion similarity issues in radar recognition.

Main Methods:

  • Utilized a physics-constrained TimeGAN (PC-TimeGAN) for generating high-quality, kinematically compliant UAV trajectories to augment scarce data.
  • Developed a multi-scale TCN-Transformer model incorporating multi-kernel dilated convolutions and self-attention mechanisms for comprehensive feature extraction.
Keywords:
TimeGANTransformerUAV/bird classificationfew-shot learninglow-altitude surveillance radar

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  • Implemented a joint loss function combining Focal Loss and Triplet Loss to optimize decision boundaries and enhance model generalization.

    • Main Results:

    • The proposed method achieved an 80.00% UAV recall, 3.15% false alarm rate (FAR), 64.00% precision, and 0.7111 F1-score on a measured dataset.
    • Demonstrated significant improvement in UAV recall compared to baseline methods like SVM, LSTM, GRU, Transformer, and 1D-CNN, especially with limited trajectory data.
    • Effectively reduced the false alarm rate of misclassifying birds as UAVs.

    Conclusions:

    • The integrated PC-TimeGAN and TCN-Transformer approach markedly enhances the performance of rapid track-level target classification for low-altitude surveillance radars.
    • This method offers a robust solution for improving the accuracy and reliability of UAV detection in complex radar environments.