RoArray: Towards More Robust Indoor Localization Using Sparse Recovery with Commodity WiFi

Abstract

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 Angle-of-Arrival (AoA), however, face a number of critical challenges. First, their localization accuracy degrades dramatically due to low Signal-to-Noise Ratio (SNR) and incoherent processing. Second, they tend to produce outliers when the available number of packets is low. Moreover, the prior phase calibration schemes are not multipath robust and accurate enough. All of the above degrade the robustness of localization systems. In this paper, we present ROArray, a RObust Array based system that accurately localizes a target even with low SNRs. The key insight of ROArray is to use sparse recovery and coherent processing across all available domains, including time, frequency, and spatial domains. Specifically, 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 Time-of-Arrival (ToAs) and AoAs of all the paths using multi-subcarrier OFDM measurements. Furthermore, through a novel multi-packet fusion scheme, ROArray is enabled to perform coherent estimation over multiple packets. 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 noise. In addition, ROArray includes an online phase calibration technique that can eliminate random phase offsets while keeping communication uninterrupted. Our implementation using off-the-shelf WiFi cards demonstrates that, with low SNRs, ROArray significantly outperforms state-of-the-art solutions in terms of localization accuracy; when medium or high SNRs are present, it achieves comparable accuracy.