AoA-Aware Probabilistic Indoor Location Fingerprinting Using Channel State Information
Luan Chen, Iness Ahriz, Didier Le Ruyet
Abstract
With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location-based services (ILBSs) in the field of the Internet of Things (IoT). For most pattern-matching-based LF solutions, previous works either appeal to the simply received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional Angle-of-Arrival (AoA) fingerprint can be accurately retrieved from the CSI phase through an enhanced subspace-based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches.