Mobile Localization Based on Received Signal Strength and Pearson's Correlation Coefficient
Huiyu Liu,
Yunzhou Zhang,
Xiaolin Su,
Xintong Li and
Ning Xu
International Journal of Distributed Sensor Networks, 2015, vol. 11, issue 8, 157046
Abstract:
Being applicable for almost every scenario, mobile localization based on cellular network has gained increasing interest in recent years. Since received signal strength indication (RSSI) information is available in all mobile phones, RSSI-based techniques have become the preferred method for GSM localization. Although the GSM standard allows for a mobile phone to receive signal strength information from up to seven base stations (BSs), most of mobile phones only use the information of the associated cell as its estimated position. Therefore, the accuracy of GSM localization is seriously limited. In this paper, an algorithm for GSM localization is proposed with RSSI and Pearson's correlation coefficient (PCC). The information of seven cells, including the serving cell and six neighboring cells, is used to accurately estimate the mobile location. With redundant information, the proposed algorithm restrains the error of Cell-ID and shows good robustness against environmental change. Without any additional device or prior statistical knowledge, the proposed algorithm is implementable on common mobile devices. Furthermore, in the practical test, its maximum error is below 550 m, which is 100 m better than that of Cell-ID, and the mean error is below 150 m, which is 250 m better than Cell-ID.
Date: 2015
References: Add references at CitEc
Citations:
Downloads: (external link)
https://journals.sagepub.com/doi/10.1155/2015/157046 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:11:y:2015:i:8:p:157046
DOI: 10.1155/2015/157046
Access Statistics for this article
More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().