Cross-frequency training with adversarial learning for radar micro-Doppler signature classification (Rising Researcher)

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

doi:10.1117/12.2559155

11 May 2020 Cross-frequency training with adversarial learning for radar micro-Doppler signature classification (Rising Researcher)

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

Author Affiliations +

Proceedings Volume 11408, Radar Sensor Technology XXIV; 114080A (2020) https://doi.org/10.1117/12.2559155
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

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.

Conference Presentation

Citation Download Citation

Sevgi Z. Gurbuz, M. Mahbubur Rahman, Emre Kurtoglu, Trevor Macks, and Francesco Fioranelli "Cross-frequency training with adversarial learning for radar micro-Doppler signature classification (Rising Researcher)", Proc. SPIE 11408, Radar Sensor Technology XXIV, 114080A (11 May 2020); https://doi.org/10.1117/12.2559155

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available