WiFi Fingerprinting Based Indoor Localization with Autonomous Survey and Machine Learning

Download or read book WiFi Fingerprinting Based Indoor Localization with Autonomous Survey and Machine Learning written by Minh Tu Hoang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The demand for accurate localization under indoor environments has increased dramatically in recent years. To be cost-effective, most of the localization solutions are based on the WiFi signals, utilizing the pervasive deployment of WiFi infrastructure and availability of the WiFi enabled mobile devices. In this thesis, we develop completed indoor localization solutions based on WiFi fingerprinting and machine learning approaches with two types of WiFi fingerprints including received signal strength indicator (RSSI) and channel state information (CSI). Starting from the low complexity algorithm, we propose a soft range limited K nearest neighbours (SRL-KNN) to address spatial ambiguity and the fluctuation of WiFi signals. SRL-KNN exploits RSSI and scales the fingerprint distance by a range factor related to the physical distance between the user's previous position and the reference location in the database. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Besides, the idea of the soft range limiting factor can be applied to all of the existed probabilistic methods, i.e., parametric and nonparametric methods, to improve their performances. A semi-sequential short term memory step is proposed to add to the existed probabilistic methods to reduce their spatial ambiguity of fingerprints and boost significantly their localization accuracy. In the following research phase, instead of locating user's position one at a time as in the cases of conventional algorithms, our recurrent neuron networks (RNNs) solution aims at trajectory positioning and takes into account of the relation among RSSI measurements in a trajectory. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. Next, the problem of localization using only one single router is analysed. CSI information will be adopted along with RSSI to enhance the localization accuracy. Each of the reference point (RP) is presented by a group of CSI measurements from several WiFi subcarriers which we call CSI images. The combination of convolutional neural network (CNN) and LSTM model is proposed. CNN extracts the useful information from several CSI values (CSI images), and then LSTM will exploit this information in sequential timesteps to determine the user's location. Finally, a fully practical passive indoor localization is proposed. Most of the conventional methods rely on the collected WiFi signal on the mobile devices (active information), which requires a dedicated software to be installed. Different from them, we leverage the received data of the routers (passive information) to locate the position of the user. The localization accuracy is investigated through experiments with several phones, e.g., Nexus 5, Samsung, Iphone and HTC, in hundreds of testing locations. The experimental results demonstrate that our proposed localization scheme achieves an average localization error of around 1.5 m when the phone is in idle mode, and approximately 1 m when it actively transmits data.