BIFROST: Reinventing WiFi Signals Based on Dispersion Effect for Accurate Indoor Localization

2023-11-12

ACM International Conference on Embedded Networked Sensor Systems

AI Summary

To address the low accuracy in indoor WiFi localization caused by scarce line-of-sight (LoS) signals, this paper proposes a hardware-software co-design solution. First, it exploits the dispersion characteristics of leaky-wave antennas (LWAs) to actively synthesize orthogonal-polarized, angle-resolvable LoS signals—a novel approach. Second, it leverages polarization mismatch to decouple multi-antenna angle-of-arrival (AoA) estimates. Third, it integrates fine-grained time-frequency domain channel state information (CSI) feature modeling with a multipath-robust localization algorithm to reconstruct WiFi signal semantics for localization-specific requirements. Experimental results show a median localization error of only 0.81 m—52.35% lower than SpotFi. When jointly deployed with existing systems, the error further decreases by 33.54%. The method significantly enhances both accuracy and practicality in complex indoor environments.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

WiFi-based device localization is a key enabling technology for smart applications, which has attracted numerous research studies in the past decade. Most of the existing approaches rely on Line-of-Sight (LoS) signals to work, while a critical problem is often neglected: In the real-world indoor environments, WiFi signals are everywhere, but very few of them are usable for accurate localization. As a result, the localization accuracy in practice is far from being satisfactory. This paper presents Bifrost, a novel hardwaresoftware co-design for accurate indoor localization. The core idea of Bifrost is to reinvent WiFi signals, so as to provide sufficient LoS signals for localization. This is realized by exploiting the dispersion effect of signals emitted by the leaky wave antenna (LWA). We present a low-cost plug-in design of LWA that can generate orthogonal polarized signals: On one hand, LWA disperses signals of different frequencies to different angles, thus providing Angle-of-Arrival (AoA) information for the localized target. On the other hand, the target further leverages the antenna polarization mismatch to distinguish AoAs from different LWAs. In the software layer, fine-grained information in Channel State Information (CSI) is exploited to cope with multipath and noise. We implement Bifrost and evaluate its performance under various settings. The results show that the median localization error of Bifrost is 0.81m, which is 52.35% less than that of SpotFi, a state-of-the-art approach. SpotFi, when combined with Bifrost to work in the realistic settings, can reduce the localization error by 33.54%.

Problem

• Enhances WiFi-based indoor localization accuracy using dispersion effect
• Addresses lack of usable Line-of-Sight signals in real-world environments
• Leverages leaky wave antenna and polarization for Angle-of-Arrival estimation

Innovation

• Uses leaky wave antenna for signal dispersion
• Exploits polarization mismatch for AoA distinction
• Leverages CSI for multipath and noise handling