Chetty, K;
Julier, S;
Shi, F;
(2019)
Passive Activity Classification Using Just WiFi Probe Response Signals.
In:
Proceedings of the 2019 IEEE Radar Conference (RadarConf).
IEEE: Boston, MA, USA.
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Abstract
Passive WiFi radar shows significant promise for a wide range of applications in both security and healthcare owing to its detection, tracking and recognition capabilities. However, studies examining micro-Doppler classification using passive WiFi radar have relied on manually stimulating WiFi access points to increase the bandwidths and duty-cycles of transmissions; either through file-downloads to generate high data-rate signals, or increasing the repetition frequency of the WiFi beacon signal from its default setting. In real-world scenarios, both these approaches would require user access to the WiFi network or WiFi access point through password authentication, and therefore involve a level of cooperation which cannot always be relied upon e.g. in law-enforcement applications. In this research, we investigate WiFi activity classification using just WiFi probe response signals which can be generated using a low-cost off-the-shelf secondary device (Raspberry Pi) eliminating the requirement to actually connect to the WiFi network. This removes the need to have continuous data traffic in the network or to modify the firmware configuration to manipulate the beacon signal interval, making the technology deployable in all situations. An activity recognition model based on a convolutional neural network resulted in an overall classification accuracy of 75% when trained from scratch using 300 measured WiFi proberesponse samples across 6 classes. This value is then increased to 82%, with significantly less training when adopting a transfer learning approach: initial training using WiFi data traffic signals, followed by fine-tuning using probe response signals
| Type: | Proceedings paper |
|---|---|
| Title: | Passive Activity Classification Using Just WiFi Probe Response Signals |
| Event: | 2019 IEEE International Radar Conference |
| Location: | Boston, US |
| Dates: | 22 April 2019 - 26 April 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/RADAR.2019.8835660 |
| Publisher version: | https://doi.org/10.1109/RADAR.2019.8835660 |
| 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. |
| Keywords: | Activity Recognition, Passive WiFi Doppler Radar, Probe Response, Transfer Learning |
| 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 Computer 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-pp.ucl.ac.uk/id/eprint/10072811 |
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