A real-time fingerprint-based indoor positioning using deep learning and preceding states

In fingerprint-based positioning methods, the received signal strength (RSS) vectors from access points are measured at reference points and saved in a database. Then, this dataset is used for the training phase of a pattern recognition algorithm. Several noise types impact the signals in radio channels, and RSS values are corrupted correspondingly. These noises can be mitigated by averaging the RSS samples. In real-time applications, the users cannot wait to collect uncorrelated RSS samples to calculate their average in the online phase of the positioning process. In this paper, we propose a solution for this problem by leveraging the distribution of RSS samples in the offline phase and the preceding state of the user in the online phase. In the first step, we propose a fast and accurate positioning algorithm using a deep neural network (DNN) to learn the distribution of available RSS samples instead of averaging them at the offline phase. Then, the similarity of an online RSS sample to the RPs’ fingerprints is obtained to estimate the user’s location. Next, the proposed DNN model is combined with a novel state-based positioning method to more accurately estimate the user’s location. Extensive experiments on both benchmark and our collected datasets in two different scenarios (single RSS sample and many RSS samples for each user in the online phase) verify the superiority of the proposed algorithm compared with traditional regression algorithms such as deep neural network regression, Gaussian process regression, random forest, and weighted KNN

RADIO ANALYTICS FOR HUMAN ACTIVITY MONITORING AND INDOOR TRACKING

With the rapid development of the Internet of Things (IoT), wireless sensing has found wide applications from wellbeing monitoring, activity recognition, to indoor tracking. In this dissertation, we investigate the problem of wireless sensing for IoT applications using only ambient radio signals, e.g., WiFi, LTE, and 5G. In particular, our work mainly focuses on passive speed estimation, motion detection, sleep monitoring, and indoor tracking for wireless sensing. In this dissertation, we first study the problem of indoor speed estimation using WiFi channel state information (CSI). We develop the statistical electromagnetic (EM) wave theory for wireless sensing and establish a link between the autocorrelation function (ACF) of the physical layer CSI and the speed of a moving object. Based on the developed statistical EM wave theory for wireless sensing, we propose a universal low-complexity indoor speed estimation system leveraging CSI, which can work in both device-free and device-based situations. The proposed speed estimator differs from the other schemes requiring strong line-of-sight conditions between the source and observer in that it embraces the rich-scattering environment typical for indoors to facilitate highly accurate speed estimation. Moreover, as a calibration-free system, it saves the users' efforts from large-scale training and fine-tuning of system parameters. The proposed speed estimator can enable many IoT applications, e.g., gait monitoring, fall detection, and activity recognition. Then, we also study the problem of indoor motion detection using CSI. The statistical behaviors of the CSI dynamics when motion presents can be characterized by the developed statistical EM theory for wireless sensing. We formulate the motion detection problem as a hypothesis testing problem and also derive the relationship between the detection rate and false alarm rate for motion detection, which is independent of locations, environments and motion types. Thus, the proposed motion detection system can work in most indoor environments, without any scenario-tailored training efforts. Extensive experiments conducted in several facilities show that the proposed system can achieve better detection performance compared to the existing CSI-based motion detection systems while maintaining a much larger coverage and a much lower false alarm rate. This dissertation also focuses on sleep monitoring using CSI. First, we build a statistical model for maximizing the signal-to-noise (SNR) ratio of breathing signal, which accounts for all reflecting and scattering multipaths, allowing highly accurate and instantaneous breathing estimation with best-ever performance achieved on commodity devices. Our results demonstrate that the proposed breathing estimator yields a median absolute error of 0.47 bpm and a 95%-tile error of only 2.92 bpm for breathing estimation, and detects breathing robustly even when a person is 10m away from the WiFi link, or behind a wall. Then, we apply machine learning algorithms on the extracted features from the estimated breathing rates to classify different sleep stages, including wake, rapid eye movement (REM), and non-REM (NREM), which was previously only possible with dedicated hardware. Experimental results show that the proposed sleep monitoring system achieves sleep staging accuracy of 88%, outperforming advanced solutions using contact sensor or radar. The last work of this dissertation considers the problem of indoor tracking using CSI. First, we leverage a stationary and location-independent property of the time-reversal (TR) focusing effect of radio signals for highly accurate moving distance estimation, which plays a key role in the proposed indoor tracking system. Together with the direction estimation based on inertial measurement unit and location correction using the constraints from the floorplan, the proposed indoor tracking system is shown to be able to track a moving object with decimeter-level accuracy in different environments

Indoor Positioning Using Acoustic Pseudo-Noise Based Time Difference of Arrival

The Global Positioning System (GPS) provides good precision on a global scale, but is not suitable for indoor applications. Indoor positioning systems (IPS) aim to provide high precision position information in an indoor environment. IPS has huge market opportunity with a growing number of commercial and consumer applications especially as Internet of Things (IoT) develops. This paper studies an IPS approach using audible sound and pseudo-noise (PN) based time difference of arrival (TDoA). The system’s infrastructure consists of synchronized speakers. The object to be located, or receiver, extracts TDoA information and uses multilateration to calculate its position. The proposed IPS utilizes sound waves since they travel much slower compared to electromagnetic waves, allowing for easier measurements. Additionally, the audible spectrum has a large availability of low directivity speakers and microphones allowing for a large coverage area compared to highly directive ultrasonic transceivers. This paper experimentally evaluates the feasibility of the proposed IPS

Introduction to Radio-frequency Identification (RFID)

This project presents the fundamental aspects of the RFID (Radio-frequency identifica-tion) technology used to establish wireless communications. The project focuses on thepassive mode of RFID, where the receiver does not have any power supply, which allowsits miniaturization and low cost.On the other hand, two articles from different authors are analyzed. The first consists onthe design of a very low power passive receiver through various techniques of optimizationin the hardware’s manufacture.In the second, a drone is used as an intermediate element between the transmitter and thereceiver to extend up to 10 times the typical range of passive RFID. The study focuses onthe treatment of the RF signal to considerably eliminate the interferences and to preciselylocalize the receiver.Outgoin

Wi-Fi based people tracking in challenging environments

People tracking is a key building block in many applications such as abnormal activity detection, gesture recognition, and elderly persons monitoring. Video-based systems have many limitations making them ineffective in many situations. Wi-Fi provides an easily accessible source of opportunity for people tracking that does not have the limitations of video-based systems. The system will detect, localise, and track people, based on the available Wi-Fi signals that are reflected from their bodies. Wi-Fi based systems still need to address some challenges in order to be able to operate in challenging environments. Some of these challenges include the detection of the weak signal, the detection of abrupt people motion, and the presence of multipath propagation. In this thesis, these three main challenges will be addressed. Firstly, a weak signal detection method that uses the changes in the signals that are reflected from static objects, to improve the detection probability of weak signals that are reflected from the person’s body. Then, a deep learning based Wi-Fi localisation technique is proposed that significantly improves the runtime and the accuracy in comparison with existing techniques. After that, a quantum mechanics inspired tracking method is proposed to address the abrupt motion problem. The proposed method uses some interesting phenomena in the quantum world, where the person is allowed to exist at multiple positions simultaneously. The results show a significant improvement in reducing the tracking error and in reducing the tracking delay

Advanced Trends in Wireless Communications

Physical limitations on wireless communication channels impose huge challenges to reliable communication. Bandwidth limitations, propagation loss, noise and interference make the wireless channel a narrow pipe that does not readily accommodate rapid flow of data. Thus, researches aim to design systems that are suitable to operate in such channels, in order to have high performance quality of service. Also, the mobility of the communication systems requires further investigations to reduce the complexity and the power consumption of the receiver. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers rather than researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics