AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

TL;DR

This paper introduces an AoA-aware probabilistic indoor location fingerprinting method using channel state information, combining AR modeling of CSI amplitude and AoA estimation to improve accuracy in complex indoor environments.

Contribution

The paper proposes a novel fingerprinting approach that leverages AR modeling of CSI amplitude and AoA estimation, enhancing indoor localization accuracy over existing methods.

Findings

Outperforms previous indoor localization techniques in accuracy.

Uses AR modeling entropy of CSI amplitude for robust fingerprinting.

Employs a bivariate kernel regression for precise location inference.

Abstract

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple 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 an 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 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.