Smart Probabilistic Approach with RSSI Fingerprinting for Indoor Localization

被引：0

作者：

Njima, Wafa ^{[1
,2
]}

Ahriz, Iness ^{[2
]}

Zayani, Rafik ^{[1
]}

Terre, Michel ^{[2
]}

Bouallegue, Ridha R. ^{[1
]}

机构：

[1] Carthage Univ, Supcom, Innovcom, Tunis, Tunisia

[2] CNAM, LAETITIA, CEDRIC, Paris, France

来源：

2017 25TH INTERNATIONAL CONFERENCE ON SOFTWARE, TELECOMMUNICATIONS AND COMPUTER NETWORKS (SOFTCOM) | 2017年

关键词：

Localization; RSSI fingerprinting; entropy; AP selection;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper introduces an efficient probabilistic approach with RSSI fingerprinting for Indoor Localization. A Shannon's Entropy based access points (APs) selection is considered. Once the APs selection is performed, a probability is assigned to each training fingerprint based on RSSI measurements. Then, the user's location is estimated as a combination of training positions weighted with their corresponding probabilities. The proposed approach is performed on the UJIndoorLoc database. It shows good performances with lower computing complexity compared to others studied in literature.

引用

页码：194 / 199

页数：6

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