Multiple Model-based Indoor Localization via Bluetooth Low Energy and Inertial Measurement Unit Sensors

Ubiquitous presence of smart connected devices coupled with evolution of Artificial Intelligence (AI) within the field of Internet of Things (IoT) have resulted in emergence of innovative ambience awareness concepts such as smart homes and smart cities. In particular, IoT-based indoor localization has gained significant popularity, given the expected widespread implementation of 5G network, to satisfy the ever increasing requirements of Location-based Services (LBS) and Proximity Based Services (PBS). LBSs and PBSs have found several applications under different circumstances such as localization profiling for human resource management; navigation assistant applications in smart buildings/hospitals, and; proximity based advertisement and marketing. The focus of this thesis is, therefore, on design and implementation of efficient and accurate indoor localization processing and learning techniques. In particular, the thesis focuses on the following three positioning frameworks: (i) \textit{Bluetooth Low Energy (BLE)-based Indoor Localization}, which uses the pathloss model to estimate the user's location; (ii) \textit{Inertial Measurement Unit (IMU)-based Indoor Positioning}, where smart phone's $3$ axis inertial sensors are utilized to iteratively estimate the headings and steps of the target, and; (iii) \textit{Pattern Recognition-based Indoor Localization}, which uses Deep Neural Networks (DNNs) to estimate the performed actions and find the user's location. With regards to Item (i), the thesis evaluates effects of the orientation of target's phone, Line of Sight (LOS) / Non Line of Sight (NLOS) signal propagation, and presence of obstacles in the environment on the BLE-based distance estimates. Additionally, a fusion framework, combining Particle Filtering with K-Nearest Neighbors (K-NN) algorithm, is proposed and evaluated based on real datasets collected through an implemented LBS platform. With regards to Item (ii), an orientation detection and multiple-modeling framework is proposed to refine Received Signal Strength Indicator (RSSI) fluctuations by compensating negative orientation effects. The proposed data-driven and orientation-free modeling framework provides improved localization results. With regards to Item (iii), the focus is on classifying actions performed by a user using Long Short Term Memory (LSTM) architectures. To address issues related to cumulative error of Pedestrian Dead Reckoning (PDR) solutions, three Online Dynamic Window (ODW) assisted LSTM positioning frameworks are proposed. The first model, uses a Natural Language Processing (NLP)-inspired Dynamic Window (DW) approach, which significantly reduces the computation time required for Real Time Localization Systems (RTLS). The second framework is developed based on a Signal Processing Dynamic Window (SP-DW) approach to further reduce the required processing time of the two stage LSTM based indoor localization. The third model, referred to as the SP-NLP, combines the first two models to further improve the overall achieved accuracy

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Multiple Model Bayesian Estimation for BLE-based Localization and RL-based Decision Support of Autonomous Agents

With the rapid emergence of Internet of Things (IoT), we are more and more surrounded by smart connected devices (agents) with integrated sensing, processing, and communication capabilities. In particular, IoT-based positioning has become of primary importance for providing advanced Location-based Services (LBSs) in indoor environments. Several LBSs have been developed recently such as navigation assistance in hospitals, localization/tracking in smart buildings, and providing assistive services via autonomous agents collectively act as an Internet of Robotic Things (IoRT). The focus of the thesis is on the following two research topics when it comes to autonomous agents providing LBSs in indoor environments: (i) Self-Localization, which is the autonomous agent’s ability to obtain knowledge of its own location, and; (ii) Localized Decision Support System, which refers to an autonomous agent’s ability to perform optimal actions towards achieving pre-defined objectives. With regards to Item (i), the thesis develops innovative localization solutions based on Bluetooth Low Energy (BLE), referred to Bluetooth Smart. Given unavailability of Global Positioning System (GPS) in indoor environments, BLE has attracted considerable attention due to its low cost, low energy consumption, and widespread availability in smart hand-held devices. Because of multipath fading and fluctuations in the indoor environment, however, BLE-based localization approaches fail to achieve high accuracies. To address these challenges, different linear and non-linear Bayesian-based estimation frameworks are proposed in this thesis. Among which, the thesis proposes a novel Multiple-Model and BLE-based tracking framework, referred to as the STUPEFY. The proposed STUPEFY framework uses set-valued information and is designed by coupling a non-linear Bayesian-based estimation model (Box Particle Filter) with fingerprinting-based methodologies to improve the overall localization accuracy. With regards to the Item (ii), there has been an increasing surge of interest on development of advanced Reinforcement Learning (RL) systems. The objective is development of intelligent approaches to learn optimal control policies directly from smart agents’ interactions with the environment. In this regard, Deep Neural Networks (DNNs) provide an attractive modeling mechanism to approximate the value function using sample transitions. DNN-based solutions, however, suffer from high sensitivity to parameter selection, are prone to overfitting, and are not very sample efficient. As a remedy to the aforementioned problems, the thesis proposes an innovative Multiple-Model Kalman Temporal Difference (MM-KTD) framework, which adapts the parameters of the filter using the observed states and rewards. Moreover, an active learning method is proposed to enhance the sampling efficiency of the overall system. The proposed MM-KTD framework can learn the optimal policy with significantly reduced number of samples as compared to its DNN-based counterparts