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22 October 2024 Few-shot generative adversarial network for radar micro-doppler spectrogram synthesis with application to human activity recognition
Feifan Wei, Tiecheng Song, Xiao Huang, Chenqiang Gao
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Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 1327405 (2024) https://doi.org/10.1117/12.3038540
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China
Abstract
Generative adversarial networks (GANs) have been recently introduced to synthesize radar micro-Doppler spectrograms for data augmentation. However, traditional GANs easily overfit with limited training data and they are hard to ensure that the synthetic spectrograms have high kinematic fidelity (e.g., continuous activity is suddenly interrupted in the time dimension). To address these issues, we propose a physics-aware few-shot generative adversarial network (PFGAN) to synthesize high-quality spectrograms with limited radar data. Our main contributions are two-fold. First, we design an attention ranking-based local fusion module (ARLFM). ARLFM learns to select important local features for matching and replacement, which considers the distribution characteristics of micro Doppler signatures. Second, we improve the kinematic fidelity of synthetic spectrograms by designing a multi-scale envelope-extraction module (MEM) where three types of spectrogram envelopes are extracted at different resolutions to reflect physical motion information. Experiments show that PFGAN can generate diverse spectrograms using few samples with high kinematic fidelity. The effectiveness of synthetic spectrograms is also demonstrated for human activity recognition tasks.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Feifan Wei, Tiecheng Song, Xiao Huang, and Chenqiang Gao "Few-shot generative adversarial network for radar micro-doppler spectrogram synthesis with application to human activity recognition", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 1327405 (22 October 2024); https://doi.org/10.1117/12.3038540
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KEYWORDS
Gallium nitride
Radar
Microelectromechanical systems
Kinematics
Data modeling
Education and training
Image quality
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