Mobile Localization Based on Received Signal Strength and Pearson's Correlation Coefficient
Author(s) -
Huiyu Liu,
Yunzhou Zhang,
Xiaolin Su,
Xintong Li,
Ning Xu
Publication year - 2015
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2015/157046
Subject(s) - gsm , computer science , robustness (evolution) , correlation coefficient , base station , mobile station , pearson product moment correlation coefficient , signal strength , cellular network , mobile telephony , mobile phone , real time computing , data mining , computer network , telecommunications , statistics , mobile radio , machine learning , wireless , mathematics , biochemistry , chemistry , gene
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.
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