TY - GEN
T1 - Context awareness with ambient FM signal using multi-domain features
AU - Wang, Jie
AU - Feng, Xueyan
AU - Gao, Qinghua
AU - Yue, Hao
AU - Fang, Yuguang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2016
Y1 - 2016
N2 - Context awareness plays an important role in many emerging applications, such as mobile computing and smart space. Since FM signal is ubiquitous, it has been recognized as an attractive and promising technique to realize context awareness. When a target is at different locations or performs different activities, it will exert different influence on the FM signal around it. Therefore, it is possible to deduce its location and activity by analysing its influence on the FM signal. However, FM signal is extremely weak and noisy, which makes it a challenging task to achieve high-performance context awareness. In this paper, we propose a new method for improving the performance of an FM-based context-aware system using multi-domain features. Specifically, we extract signal features not only from the time domain, but also from the wavelet domain, the frequency domain, and the space domain, and construct robust and discriminative multi-domain features to characterize the FM signal. Furthermore, we also model context awareness as a classification problem and develop a robust iterative sparse representation classification algorithm to efficiently solve this problem. Extensive experiments performed in a 7.2m×10.8m clutter indoor laboratory with one multi- channel FM receiver demonstrate that the proposed schemes could achieve more than 90% accuracy of location estimation and activity recognition when 3 antennas are used.
AB - Context awareness plays an important role in many emerging applications, such as mobile computing and smart space. Since FM signal is ubiquitous, it has been recognized as an attractive and promising technique to realize context awareness. When a target is at different locations or performs different activities, it will exert different influence on the FM signal around it. Therefore, it is possible to deduce its location and activity by analysing its influence on the FM signal. However, FM signal is extremely weak and noisy, which makes it a challenging task to achieve high-performance context awareness. In this paper, we propose a new method for improving the performance of an FM-based context-aware system using multi-domain features. Specifically, we extract signal features not only from the time domain, but also from the wavelet domain, the frequency domain, and the space domain, and construct robust and discriminative multi-domain features to characterize the FM signal. Furthermore, we also model context awareness as a classification problem and develop a robust iterative sparse representation classification algorithm to efficiently solve this problem. Extensive experiments performed in a 7.2m×10.8m clutter indoor laboratory with one multi- channel FM receiver demonstrate that the proposed schemes could achieve more than 90% accuracy of location estimation and activity recognition when 3 antennas are used.
KW - Activity recognition
KW - Context awareness
KW - Localization
KW - Wireless networks
UR - https://www.scopus.com/pages/publications/85015439976
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85015439976&origin=recordpage
U2 - 10.1109/GLOCOM.2016.7841694
DO - 10.1109/GLOCOM.2016.7841694
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781509013289
T3 - 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
BT - 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
PB - IEEE
T2 - 59th IEEE Global Communications Conference, GLOBECOM 2016
Y2 - 4 December 2016 through 8 December 2016
ER -