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|Title:||Intelligent indoor localization based on RF signals||Authors:||Qi, Jingya||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2019||Abstract:||In recent years, localization using RF signal in existing communication network or with limited infrastructure is studied extensively. As WiFi and other RF devices have been widely deployed in buildings, it is natural to use them for localization as well. In this project, machine learning and data analytics will be studied to improve indoor localization accuracy based on radio frequency (RF) signals. Currently, some relevant researches have been conducted on dataset including Received signal strength indication (RSSI) features or Channel State Information (CSI) using Support Vector Machine, neural network and K-nearest neighbors (KNN) method. In this project, 2 datasets have been implemented using SVM. Most of the work is focusing on UJIIndoorLoc dataset, training, testing results as well as the analysis of the results will be included.||URI:||http://hdl.handle.net/10356/77255||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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