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Deep neural networks have become increasingly popular in radar micro-Doppler classification; yet, a key challenge, which has limited potential gains, is the lack of large amounts of measured data that can facilitate the design of deeper networks with greater robustness and performance. Several approaches have been proposed in the literature to address this problem, such as unsupervised pre-training and transfer learning from optical imagery or synthetic RF data. This work investigates an alternative approach to training which involves exploitation of “datasets of opportunity” – micro-Doppler datasets collected using other RF sensors, which may be of a different frequency, bandwidth or waveform - for the purposes of training. Specifically, this work compares in detail the cross-frequency training degradation incurred for several different training approaches and deep neural network (DNN) architectures. Results show a 70% drop in classification accuracy when the RF sensors for pre-training, fine-tuning, and testing are different, and a 15% degradation when only the pre-training data is different, but the fine-tuning and test data are from the same sensor. By using generative adversarial networks (GANs), a large amount of synthetic data is generated for pre-training. Results show that cross-frequency performance degradation is reduced by 50% when kinematically-sifted GAN-synthesized signatures are used in pre-training.
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Physics-Aware Design of Multi-Branch GAN for Human RF Micro-Doppler Signature Synthesis
Generative adversarial networks (GANs) have been recently proposed for the synthesis of RF micro-Doppler signatures to mitigate the problem of low sample support and enable the training of deeper neural networks (DNNs) for improved RF signal classification. However, when applied to human micro-Doppler signatures for gait analysis, GANs suffer from systemic kinematic discrepancies that degrade performance. As a solution to this problem, this paper proposes the design of a physics-aware loss function and multi-branch GAN architecture. Our results show that RF gait signatures synthesized using the proposed approached have greater correlation and similarity to measured RF gait signatures, while also improving the accuracy in classifying five different gaits.
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- Award ID(s):
- 1932547
- PAR ID:
- 10296378
- Date Published:
- Journal Name:
- IEEE Radar Conference
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/RadarConf2147009.2021.9455194
