Lookup Lateration: Mapping of Received Signal Strength to Position for Geo-Localization in Outdoor Urban Areas

TL;DR

This paper introduces lookup lateration, a novel geo-localization method that combines the speed of triangulation with the accuracy of fingerprinting, outperforming existing methods in urban outdoor environments.

Contribution

The paper proposes lookup lateration, a new scalable and accurate RSS-based localization method that integrates advantages of triangulation and fingerprinting without their limitations.

Findings

Lookup lateration is two orders of magnitude faster than fingerprinting.

It achieves higher localization accuracy than fingerprinting.

On the Urban Hannover Scenario dataset, LL reduces errors significantly.

Abstract

The accurate geo-localization of mobile devices based upon received signal strength (RSS) in an urban area is hindered by obstacles in the signal propagation path. Current localization methods have their own advantages and drawbacks. Triangular lateration (TL) is fast and scalable but employs a monotone RSS-to-distance transformation that unfortunately assumes mobile devices are on the line of sight. Radio frequency fingerprinting (RFP) methods employ a reference database, which ensures accurate localization but unfortunately hinders scalability. Here, we propose a new, simple, and robust method called lookup lateration (LL), which incorporates the advantages of TL and RFP without their drawbacks. Like RFP, LL employs a dataset of reference locations but stores them in separate lookup tables with respect to RSS and antenna towers. A query observation is localized by identifying common locations in only associating lookup tables. Due to this decentralization, LL is two orders of magnitude faster than RFP, making it particularly scalable for large cities. Moreover, we show that analytically and experimentally, LL achieves higher localization accuracy than RFP as well. For instance, using grid size 20 m, LL achieves 9.11 m and 55.66 m, while RFP achieves 72.50 m and 242.19 m localization errors at 67\% and 95\%, respectively, on the Urban Hannover Scenario dataset.