Direct-path based fingerprint extraction algorithm for indoor localization

被引：5
作者：
Zhu, Dali [1 ,2 ]
Zhao, Bobai [1 ,2 ]
Wang, Siye [1 ,2 ]
Wu, Di [1 ,2 ]
机构：
[1] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 15TH EAI INTERNATIONAL CONFERENCE ON MOBILE AND UBIQUITOUS SYSTEMS: COMPUTING, NETWORKING AND SERVICES (MOBIQUITOUS 2018) | 2018年
基金：
中国国家自然科学基金;
关键词：
Indoor Localization; Channel State Information; Fingerprint Extraction; KALMAN FILTER; LOCATION;
D O I：
10.1145/3286978.3286993
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
At present, there has been a booming interest in utilizing Channel State Information (CSI) extracted from MIMO-OFDM PHY layer to achieve precise indoor localization. Compared with Received Signal Strength Indicator (RSSI), CSI as a fine-grained feature has a better performance on expressing the spatial and temporal features of wireless signal. As a result, CSI is more sensitive to the noise interference and multi-path. In this paper, we present a direct-path based fingerprint extraction algorithm for indoor localization in noisy and multi-path indoor environment. Our proposed algorithm firstly extracts the amplitude and phase measurements of direct-path from the raw CSI, and then calculates the unique fingerprint feature according to the filtered CSI. The experimental results show that our proposed algorithm improves the positioning accuracy up to 23.5% in complex indoor multi-path environment.
引用
收藏
页码：11 / 18
页数：8
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