Cross-frequency training with adversarial learning for radar micro-Doppler signature classification

Sevgi Z. Gurbuz, M. Mahbubur Rahman, Emre Kurtoglu, Trevor Macks, Francesco Fioranelli

Research output: Contribution to journalConference articleScientificpeer-review

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Abstract

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.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalProceedings of SPIE- International Society for Optical Engineering
Volume11408
DOIs
Publication statusPublished - 2020
EventSPIE Defense + Commercial Sensing 2020 Digital Forum, Online only
- , United States
Duration: 27 Apr 20208 May 2020

Keywords

  • Generative adversarial networks
  • Micro-Doppler classification
  • Radar networks
  • Transfer learning

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