Context Awareness with Ambient FM Signal Using Multi-Domain Features

Abstract

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.