Computer Vision-assisted Single-antenna and Single-anchor RSSI Localization Harnessing Dynamic Blockage Events

TL;DR

This paper introduces a novel method combining computer vision and RSSI blockage detection to enable accurate indoor localization using only a single antenna and RF anchor, overcoming traditional limitations.

Contribution

The paper presents a new approach that leverages CV to estimate obstacle positions and blockage timings, allowing single-antenna RSSI localization without multiple anchors or antennas.

Findings

Achieved less than 1.0 m localization error indoors.

Comparable accuracy to traditional triangulation with multiple anchors.

Demonstrated feasibility of single-antenna, single-anchor RSSI localization.

Abstract

This paper demonstrates the feasibility of single-antenna and single-RF (radio frequency)- anchor received power strength indicator (RSSI) localization (SARR-LOC) with the assistance of the computer vision (CV) technique. Generally, to perform radio frequency (RF)-based device localization, either 1) fine-grained channel state information or 2) RSSIs from multiple antenna elements or multiple RF anchors (e.g., access points) is required. Meanwhile, owing to deficiency of single-antenna and single-anchor RSSI, which only indicates a coarse-grained distance information between a receiver and a transmitter, realizing localization with single-antenna and single-anchor RSSI is challenging. Our key idea to address this challenge is to leverage CV technique and to estimate the most likely first Fresnel zone (FFZ) between the receiver and transmitter, where the role of the RSSI is to detect blockage timings. Specifically, historical positions of an obstacle that dynamically blocks the FFZ are detected by the CV technique, and we estimate positions at which a blockage starts and ends via a time series of RSSI. These estimated obstacle positions, in principle, coincide with points on the FFZ boundaries, enabling the estimation of the FFZ and localization of the transmitter. The experimental evaluation revealed that the proposed SARR-LOC achieved the localization error less than 1.0 m in an indoor environment, which is comparable to that of a conventional triangulation-based RSSI localization with multiple RF anchors.