Wireless RSSI fingerprinting localization

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• 作者：Simon Yiu ; ^{simon.yiu@nokia.com" class="auth_mail" title="E-mail the corresponding author} ; Marzieh Dashti ^{marzieh.dashti@nokia.com" class="auth_mail" title="E-mail the corresponding author} ; Holger Claussen ^{holger.claussen@nokia.com" class="auth_mail" title="E-mail the corresponding author} ; Fernando Perez-Cruz ^{fernando.perez-cruz@nokia.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Fingerprinting localization ; Location-based service (LBS) ; Received signal strength indicator (RSSI) ; Pathloss model ; Gaussian Process ; Non-parametric model ; Machine learning
• 刊名：Signal Processing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：131
• 期：Complete
• 页码：235-244
• 全文大小：655 K

文摘

Localization has attracted a lot of research effort in the last decade due to the explosion of location based service (LBS). In particular, wireless fingerprinting localization has received much attention due to its simplicity and compatibility with existing hardware. In this work, we take a closer look at the underlying aspects of wireless fingerprinting localization. First, we review the various methods to create a radiomap. In particular, we look at the traditional fingerprinting method which is based purely on measurements, the parametric pathloss regression model and the non-parametric Gaussian Process (GP) regression model. Then, based on these three methods and measurements from a real world deployment, the various aspects such as the density of access points (APs) and impact of an outdated signature map which affect the performance of fingerprinting localization are examined. At the end of the paper, the audiences should have a better understanding of what to expect from fingerprinting localization in a real world deployment.