Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Nowadays, the availability of the location information becomes a key factor in today’s communications systems for allowing location based services. In outdoor scenarios, the Mobile Terminal (MT) position is obtained with high accuracy thanks to the Global Positioning System (GPS) or to the standalone cellular systems. However, the main problem of GPS or cellular systems resides in the indoor environment and in scenarios with deep shadowing effect where the satellite or cellular signals are broken. In this paper, we will present a review over different technologies and concepts used to improve indoor localization. Additionally, we will discuss different applications based on different localization approaches. Finally, comprehensive challenges in terms of accuracy, cost, complexity, security, scalability, etc. are presente

Hybrid analog-digital processing system for amplitude-monopulse RSSI-based MiMo wifi direction-of-arrival estimation

We present a cost-effective hybrid analog digital system to estimate the Direction of Arrival (DoA) of WiFi signals. The processing in the analog domain is based on simple wellknown RADAR amplitude monopulse antenna techniques. Then, using the RSSI (Received Signal Strength Indicator) delivered by commercial MiMo WiFi cards, the DoA is estimated using the socalled digital monopulse function. Due to the hybrid analog digital architecture, the digital processing is extremely simple, so that DoA estimation is performed without using IQ data from specific hardware. The simplicity and robustness of the proposed hybrid analog digital MiMo architecture is demonstrated for the ISM 2.45GHz WiFi band. Also, the limitations with respect to multipath effects are studied in detail. As a proof of concept, an array of two MiMo WiFi DoA monopulse readers are distributed to localize the two-dimensional position of WiFi devices. This costeffective hybrid solution can be applied to all WiFi standards and other IoT narrowband radio protocols, such us Bluetooth Low Energy or Zigbee.This work was supported in part by the Spanish National Projects TEC2016-75934-C4-4-R, TEC2016-76465-C2-1-R and in part by Regional Seneca Project 19494/PI/14

Data fusion for ground target tracking in GSM networks

Positioning in mobile cellular networks is an exciting research area. The Global System for Mobile communications (GSM) network, as a widely used mobile communication standard around the world, has shown the potential to provide position information. Ground target tracking is a significant application of finding the position of a mobile station (MS). However, a GSM positioning system based on current specifications faces many difficulties to yield an accurate position estimate. Since the signals are designed by communication needs rather than positioning, the resolution of the measurements in GSM networks for positioning is coarse. The ambiguities of the position estimate arise when there are not a sufficient number of measurements available. Moreover, due to the restriction of terrain, road and traffic, the ground target often maneuvers. Therefore, data fusion approaches, which integrate redundant information from different sources, are applied in this work to obtain improved position estimation accuracy. This work focuses on the state estimation problem of the MS\u27s position given the measurements from the GSM networks and a priori road information. A data fusion solution, which integrates time of arrival (TOA) and received signal strength (RSS) measurements using an extended Kalman filter (EKF), is proposed to provide an improved position estimate. The theoretical best achievable performance, posterior Cramer-Rao lower bound (PCRLB), is derived for the data fusion approach. The PCRLB is used to demonstrate the benefits of the fusion approach and applied as a benchmark to compare different estimators. The road constraint is incorporated into the estimation process as a pseudomeasurement. Simulations of the linear and nonlinear road segments prove the advantages of the road-constrained approach. Moreover, the motion mode uncertainty problem is considered and solved by a multiple model (MM) approach. In particular, an adaptive road-constrained interacting MM (ARC-IMM) estimator, which incorporates the road information into a variable structure MM mechanism, is proposed and demonstrated to be effective and robust to provide a significantly improved position estimate.Die Ortung in Mobilfunknetzen ist ein faszinierendes Forschungsgebiet. Der in großem Umfang genutzte Mobilfunkstandard für digitale Netze Global System for Mobile communications (GSM) kann auch zur Positionsbestimmung erfolgreich eingesetzt werden. Eine der bedeutenden Anwendungen bezüglich der Ortung von Mobilstationen (MS), d.h. von Mobilfunkend-geräten, ist das sogenannte Ground-Target-Tracking, also die Zielverfolgung derselben. Im Falle eines GSM-basierten Ortungssystems, das auf den aktuellen GSM-Spezifikationen basiert, müssen viele Schwierigkeiten überwunden werden, um die Position genau schätzen zu können. Zum einen liegen - da die Signale im Wesentlichen unter Berücksichtigung der Anforderungen hinsichtlich der Kommunikation (und nicht der Ortung) entworfen wurden - die Ergebnisse der Ortung in GSM-Netzwerken nur in einer groben Auflösung vor, und im Falle einer nicht ausreichend hohen Anzahl von verfügbaren Messwerten treten Mehrdeutigkeiten bei der Positionsschätzung auf. Zum anderen führt das Ziel entsprechend dem Gelände, dem Straßenverlauf und dem Verkehr oft Bewegungsänderungen durch. In dieser Arbeit werden deshalb Datenfusionsansätze verfolgt, die redundante Messwerte aus verschiedenen Quellen berücksichtigen, um eine verbesserte Genauigkeit der Positionsschätzung zu erzielen. Im Mittelpunkt der Arbeit steht die Zustandsschätzung unter Berücksichtigung der Messwerte aus dem GSM-Netzwerk und von a priori Information zum Straßenverlauf. Es wird ein Datenfusionsansatz eingeführt, mit dem die Fusion der Messwerte aus den Verfahren Time-of-Arrival (TOA) und Received-Signal-Strength (RSS) möglich wird, um einen verbesserten Positionsschätzwert zu erhalten. Es wird dabei ein Extended-Kalman-Filter (EKF) eingesetzt. Die theoretisch beste erzielbare Genauigkeit mit dem Datenfusionsansatz wird in Form der posterior Cramér-Rao lower bound (PCRLB) abgeleitet. Die PCRLB wird herangezogen um die Vorteile des Datenfusionsansatzes zu zeigen und dient als Benchmark für den Vergleich verschiedener Verfahren. Die Information über den Straßenverlauf wird in den Schätzprozess in Form einer Pseudomessung integriert. Simulationen sowohl in linearen als auch in nichtlinearen Fällen zeigen die Vorteile dieses Ansatzes, der die Randbedingungen durch den Straßenverlauf einbezieht. Weiterhin wird das Problem der Unsicherheit bei der Auswahl der Bewegungsart im Multiple-Model (MM) - Ansatz betrachtet und gelöst. Insbesondere wird ein sogenannter Adaptive-Road-Constraint-Interacting-Multiple-Model (ARC-IMM) - Schätzer, der die Straßen-information in einen MM-Ansatz mit variabler Struktur integriert, vorgeschlagen. Es wird gezeigt, dass dieser Schätzer effizient und robust ist, und eine wesentlich verbesserte Positionsschätzung liefert

Performance analysis of cellular and ad-hoc sensor networks : theory and applications

Fifth-generation (5G) mobile networks have three main goals namely enhanced mobile broadband (eMBB), massive machine-type communication (mMTC) and ultra-reliable low latency communication (URLLC). The performance measures associated with these goals are high peak throughput, high spectral efficiency, high capacity and mobility. Moreover, achieving ubiquitous coverage, network and device energy efficiency, ultra-high reliability and ultra-low latency are associated with the performance of 5G mobile networks. One of the challenges that arises during the analysis of these networks is the randomness of the number of nodes and their locations. Randomness is an inherent property of network topologies and could occur due to communication outage, node failure, blockage or mobility of the communication nodes. One of the tools that enable analysis of such random networks is stochastic geometry, including the point process theory. The stochastic geometry and Poisson point theory allow us to build upon tractable models and study the random networks, which is the main focus of this dissertation. In particular, we focus on the performance analysis of cellular heterogeneous networks (HetNet) and ad-hoc sensor networks. We derive closed-forms and easy-to-use expressions, characterising some of the crucial performance metrics of these networks. First, as a HetNet example, we consider a three-tier hybrid network, where microwave (µWave) links are used for the first two tiers and millimetre wave (mmWave) links for the last tier. Since HetNets are considered as interference-limited networks, therefore, we also propose to improve the coverage in HetNet by deploying directional antennas to mitigate interference. Moreover, we propose an optimisation framework for the overall area spectral and energy efficiency concerning the optimal signal-to-interference ratio (SIR) threshold required for µWave and mmWave links. Results indicate that for the µWave tiers (wireless backhaul) the optimal SIR threshold required depends only on the path-loss exponent and that for the mmWave tier depends on the area of line-of-sight (LOS) region. Furthermore, we consider the average rate under coverage and show that the area spectral and energy efficiency are strictly decreasing functions with respect to the SIR threshold. Second, in ad-hoc sensor networks, coverage probability is usually defined according to a fixed detection range ignoring interference and propagation effects. Hence, we define the coverage probability in terms of the probability of detection for localisability. To this end, we provide an analysis for the detection probability and S-Localisability probability, i.e. the probability that at least S sensors may successfully participate in the localisation procedure, according to the propagation effects such as path-loss and small-scale fading. Moreover, we analyse the effect of the number of sensors S on node localisation and compare different range based localisation algorithms

Signals of Opportunity for Positioning Purposes

O ver the last years, location-based services (LBS) have become popular due to the emergence of smartphones with capabilities of positioning their user’s location on Earth at unprecedented speed and convenience. Behind such feat are the technological advances in global navigation satellite systems (GNSS), such as Galileo, Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS), Global Positioning Service (GPS) and Beidou. The easiness of smartphones and the improvement of positioning technology has driven LBS to be at the core of many business models. Some of these business models rely on the user’s location to pick him up on a car, relinquish a meal to him, offer insights on sports performance, locate items to be picked up on a warehouse, among many others.While LBS are driving the need to continuously locate the user at higher degrees of accuracy and across any environment, be it in a city park, an urban canyon or inside a corporate office, some of these environments pose a challenge for GNSS. Indoor environments are particularly challenging for GNSS due to the attenuation and strong multipath imposed by walls and building materials. Such challenges and difficulties in signal acquisition have led to the development of solutions and technologies to improve positioning in indoor environments.While there are several commercial systems available to fulfill the needs of most LBS in indoor environments, most of these are not feasible to deploy at a global scale due to their infrastructure costs. Hence, several solutions have sought to build upon existing infrastructure to provide positioning information.Building upon existing infrastructure is what leads to the main topic of this thesis, the concept of signals of opportunity (SoO). A SoO is any wireless signal that can be exploited for a positioning purpose despite its initial design seeking to fulfill a different purpose. A few examples of these signals are IEEE 802.11 signals, commonly known as WiFi, Bluetooth, digital video broadcasting - terrestrial (DVB-T) and many of the cellular signals, such as long-term evolution (LTE), universal mobile telecommunications system (UMTS) and global mobile system (GSM).The goal of this thesis is to address various challenges related to SoO for positioning. From the identification of SoO at the physical layer, how to merge them at the algorithmic level and how to put them in use for a cognitive positioning system (CPS)

Physical Layer Challenges and Solutions in Seamless Positioning via GNSS, Cellular and WLAN Systems

As different positioning applications have started to be a common part of our lives, positioning methods have to cope with increasing demands. Global Navigation Satellite System (GNSS) can offer accurate location estimate outdoors, but achieving seamless large-scale indoor localization remains still a challenging topic. The requirements for simple and cost-effective indoor positioning system have led to the utilization of wireless systems already available, such as cellular networks and Wireless Local Area Network (WLAN). One common approach with the advantage of a large-scale standard-independent implementation is based on the Received Signal Strength (RSS) measurements.This thesis addresses both GNSS and non-GNSS positioning algorithms and aims to offer a compact overview of the wireless localization issues, concentrating on some of the major challenges and solutions in GNSS and RSS-based positioning. The GNSS-related challenges addressed here refer to the channel modelling part for indoor GNSS and to the acquisition part in High Sensitivity (HS)-GNSS. The RSSrelated challenges addressed here refer to the data collection and calibration, channel effects such as path loss and shadowing, and three-dimensional indoor positioning estimation.This thesis presents a measurement-based analysis of indoor channel models for GNSS signals and of path loss and shadowing models for WLAN and cellular signals. Novel low-complexity acquisition algorithms are developed for HS-GNSS. In addition, a solution to transmitter topology evaluation and database reduction solutions for large-scale mobile-centric RSS-based positioning are proposed. This thesis also studies the effect of RSS offsets in the calibration phase and various floor estimators, and offers an extensive comparison of different RSS-based positioning algorithms

Hybrid positioning data fusion in heterogeneous networks with critical hearability

International audienceIn this paper, we propose and investigate a hybrid positioning data fusion technique for heterogeneous networks incritical transmission scenarios. The focus is on two scenarios: the small indoor scenario combining Wi-Fi andcellular systems and the small-to-mid-scale scenario composed of one located Mobile Terminal (MT) and oneanchor node (AN). More specifically, we investigate the effect of the availability of three metrics i.e. the time ofarrival (ToA), the angle of arrival (AoA), and the received signal strength-based fingerprint (RSS) on the positioning accuracywhen the number of ANs is less than three. To combine these measurements, we use a 2-level unscented Kalman Filter(UKF) in conjunction with some advanced clustering techniques based on genetic algorithms. Simulation results show thatthe proposed hybrid data fusion technique outperforms the techniques presented in the literature independently of thetransmission conditions

Endangered Languages and Languages in Danger

This peer-reviewed collection brings together the latest research on language endangerment and language rights. It creates a vibrant, interdisciplinary platform for the discussion of the most pertinent and urgent topics central to vitality and equality of languages in today’s globalised world. The novelty of the volume lies in the multifaceted view on the variety of dangers that languages face today, such as extinction through dwindling speaker populations and lack of adequate preservation policies or inequality in different social contexts (e.g. access to justice, education and research resources). There are examples of both loss and survival, and discussion of multiple factors that condition these two different outcomes. We pose and answer difficult questions such as whether forced interventions in preventing loss are always warranted or indeed viable. The emerging shared perspective is that of hope to inspire action towards improving the position of different languages and their speakers through research of this kind

Endangered Languages and Languages in Danger

This peer-reviewed collection brings together the latest research on language endangerment and language rights. It creates a vibrant, interdisciplinary platform for the discussion of the most pertinent and urgent topics central to vitality and equality of languages in today’s globalised world. The novelty of the volume lies in the multifaceted view on the variety of dangers that languages face today, such as extinction through dwindling speaker populations and lack of adequate preservation policies or inequality in different social contexts (e.g. access to justice, education and research resources). There are examples of both loss and survival, and discussion of multiple factors that condition these two different outcomes. We pose and answer difficult questions such as whether forced interventions in preventing loss are always warranted or indeed viable. The emerging shared perspective is that of hope to inspire action towards improving the position of different languages and their speakers through research of this kind