A novel fingerprinting using channel state information with MIMO–OFDM

被引：3

作者：

Lei Zhang

Enjie Ding

Zhikai Zhao

Yanjun Hu

Xin Wang

Kai Zhang

机构：

[1] China University of Mining and Technology,School of Information and Control Engineering

[2] China University of Mining and Technology,IoT Perception Mine Research Center

来源：

Cluster Computing | 2017年 / 20卷

关键词：

Indoor localization; Channel state information; Feature distance; MIMO–OFDM;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

With the increasing demand of location-based services, indoor localization based on fingerprinting has become an increasingly important technique due to its high accuracy and low hardware requirement. The paper presents a novel fingerprinting system with a fine-grained information known as channel state information (CSI). The proposed fingerprint exploits multiple antennas and subcarriers of the IEEE 802.11n network using multiple input multiple output (MIMO)–orthogonal frequency division multiplexing system and takes account into the distance of features in each fingerprint. The experimental performance of the fingerprint is compared with RSSI-based Fingerprinting system and the CSI–MIMO Fingerprinting system. The experiment results show that the system achieves the accuracy of 0.61 and 0.9 m in static and dynamic scenarios respectively.

引用

页码：3299 / 3312

页数：13

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