Abstract
This work considers the use of a passive WiFi radar (PWR) to monitor human activities. Real-time uncooperative monitoring of people has numerous applications ranging from smart cities and transport to IoT and security. In e-healthcare, PWR technology could be used for ambient assisted living and early detection of chronic health conditions. Large training datasets could drive forward machine-learning-focused research in the above applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Therefore, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data at multiple IEEE WiFi standards(IEEE 802.11g, n, and ad). We qualitatively compare the micro-Doppler signatures generated through SimHumalator with the measured signatures. To create a more realistic training dataset, we artificially add noise to our clean simulated spectrograms. A noise distribution is directly learned from real radar measurements using a Generative Adversarial Network (GAN). We observe improvements in the classification performances between 3 to 8%. Our results suggest that simulation data can be used to make adequate training data when the available measurement training support is low.
Original language | English |
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Title of host publication | 2021 IEEE International Conference on Communications Workshops |
Subtitle of host publication | (ICC Workshops) Proceesings |
Publisher | IEEE |
Number of pages | 6 |
ISBN (Print) | 978-1-7281-9441-7 |
DOIs | |
Publication status | Published - Jun 2021 |
Event | 2021 IEEE International Conference on Communications Workshops : (ICC Workshops) - Montreal, Canada Duration: 14 Jun 2021 → 23 Jun 2023 |
Conference
Conference | 2021 IEEE International Conference on Communications Workshops |
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Country/Territory | Canada |
City | Montreal |
Period | 14/06/21 → 23/06/23 |