A Hidden Markov Model for Localization Using Low-End GSM Cell Phones

TL;DR

This paper introduces a Hidden Markov Model approach for GSM localization that effectively uses limited signal data from low-end phones, significantly improving accuracy in urban and rural environments.

Contribution

It presents a novel HMM-based localization method that relies solely on the associated cell tower's signal strength, addressing limitations of low-end phones and sparse tower density.

Findings

Achieves at least 156% median error reduction in rural areas.

Achieves at least 68% median error reduction in urban areas.

Successfully implemented on Android phones with improved accuracy.

Abstract

Research in location determination for GSM phones has gained interest recently as it enables a wide set of location based services. RSSI-based techniques have been the preferred method for GSM localization on the handset as RSSI information is available in all cell phones. Although the GSM standard allows for a cell phone to receive signal strength information from up to seven cell towers, many of today's cell phones are low-end phones, with limited API support, that gives only information about the associated cell tower. In addition, in many places in the world, the density of cell towers is very small and therefore, the available cell tower information for localization is very limited. This raises the challenge of accurately determining the cell phone location with very limited information, mainly the RSSI of the associated cell tower. In this paper we propose a Hidden Markov Model based solution that leverages the signal strength history from only the associated cell tower to achieve accurate GSM localization. We discuss the challenges of implementing our system and present the details of our system and how it addresses the challenges. To evaluate our proposed system, we implemented it on Androidbased phones. Results for two different testbeds, representing urban and rural environments, show that our system provides at least 156% enhancement in median error in rural areas and at least 68% enhancement in median error in urban areas compared to current RSSI-based GSM localization systems