UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

GAN Based Noise Generation to Aid Activity Recognition when Augmenting Measured WiFi Radar Data with Simulations

Vishwakarma, S; Tang, C; Li, W; Woodbridge, K; Adve, R; Chetty, K; (2021) GAN Based Noise Generation to Aid Activity Recognition when Augmenting Measured WiFi Radar Data with Simulations. In: 2021 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE Green open access

[thumbnail of GAN Based Noise generation.pdf]
Preview
Text
GAN Based Noise generation.pdf - Accepted Version

Download (2MB) | Preview

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.

Type: Proceedings paper
Title: GAN Based Noise Generation to Aid Activity Recognition when Augmenting Measured WiFi Radar Data with Simulations
Event: ICC 2021 - 2021 IEEE International Conference on Communications (ICC)
Dates: 14 June 2021 - 23 June 2021
Open access status: An open access version is available from UCL Discovery
Publisher version: https://doi.org/10.1109/ICCWorkshops50388.2021.947...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10124798
Downloads since deposit
20Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item