Robust Indoor Wireless Localization Using Sparse Recovery
Wei Gong and Jiangchuan Liu
Simon Fraser University, Simon Fraser University

With the multi-antenna design of WiFi interfaces, phased array has become a promising mechanism for accurate WiFi localization. State-of-the-art WiFi-based solutions using AoA (Angle-of-Arrival), however, face a number of critical challenges. First, their localization accuracy degrades dramatically when the Signal-to-Noise Ratio (SNR) becomes low. Second, they do not fully utilize coherent processing across all available domains. In this paper, we present ROArray, a RObust Array based system that accurately localizes a target even with low SNRs. First, in the spatial domain, ROArray can produce sharp AoA spectrums by parameterizing the steering vector based on a sparse grid. Then, to expand into the frequency domain, it jointly estimates the ToAs (Time-of-Arrival) and AoAs of all the paths using multi-subcarrier OFDM measurements. Furthermore, through multi-packet fusion, ROArray is enabled to perform coherent estimation across the spatial, frequency, and time domains. Such coherent processing not only increases the virtual aperture size, which enlarges the number of maximum resolvable paths, but also improves the system robustness to noises. Our implementation using off-the-shelf WiFi cards demonstrates that, with low SNRs, ROArray significantly outperforms state-ofthe-art solutions in localization accuracy; when medium or high SNRs are present, it achieves comparable accuracy.