MobLoc: CSI-Based Location Fingerprinting With MUSIC

Abstract

Many CSI-based localization methods have been proposed over the last decade. Fingerprinting has been one of the highest achieving approaches due to its capacity to capture environmental characteristics that are not readily captured using classic localization mechanisms such as multilateration. However, oftentimes the proposed methods are limited by reliance on large-scale training datasets. Further, methods are rarely evaluated on nonstationary devices, which are the most common in real-world environments. In our work, we address these challenges by introducing MobLoc. We adopt MUSIC pseudospectrum-based fingerprinting, which can benefit from, but does not heavily rely upon a large number of packets for each fingerprint. To evaluate our method, we leverage a publicly available dataset of passively collected CSI measurements, DLoc (Ayyalasomayajula et al., 2020), where an emitter sends signals in motion. We also benchmark MobLoc against a series of state-of-the-art localization methods. The results demonstrate that our method outperforms SpotFi (Kotaru et al., 2015), EntLoc (Chen et al., 2019), and AngLo (Chen et al., 2020), and falls very short of achieving DLoc accuracy. On the DLoc dataset, MobLoc achieves 0.33 m median (and 0.82 m, 90th percentile) localization error in a simple environment and 1.15 m median (2.59 m, 90th percentile) localization error in a complex environment. However, despite MobLoc not exceeding DLoc's accuracy, we consider its performance as a tradeoff for computational resources required to deploy the method in a real-world environment. We anticipate that this advantage will enable the adoption of MobLoc in city-scape localization systems, where the cost of computational resources is key.