Academic literature on the topic 'Pedestrian localization'

Location based services are becoming an indispensable part of our life. The wide adoption of satellite based positioning - Global Positioning System (GPS) has practically solved the problem of outdoor localization for a wide range of scenarios. Unfortunately, satellite based positioning is not possible indoors because of weak radio signals and loss of the direct line of sight from the satellites. Therefore, significant efforts have been motivated towards finding a practical solution for indoor localization especially in regards to localizing pedestrian. Certainly the topic of indoor pedestrian positioning does not lack research, there have been several research studies also various commercial solutions have been developed. What is common for all of them is that no approach has yet made a big impact within this area (e.g. GPS for outdoor localization). The reason behind this is that they either need an expensive infrastructure deployment (e.g. Wi-Fi access points) or have specialised hardware needs (e.g. network card), or have low accuracy and low reliability or have privacy issues such that pedestrians’ location is continuously monitored without their consent. There is also a trade-off between accuracy and cost. Sensing infrastructures (e.g. Wi-Fi) involving higher investments provide better accuracy where as those involving lower investments (e.g. QR codes) provide lower accuracy. Even worse, systems could not logically localize a pedestrian that is whether they are on this room or the adjoining room separated by a dividing wall and somehow if they do, they require large amounts of infrastructure to be installed into the environment. Smartphones are little less to ubiquitous. Thus, this thesis investigates an alternative approach to indoor pedestrian localization that uses smartphones to provide accurate, reliable, low cost logical localization. A significant emphasis is given on user privacy and minimal usage of infrastructure or none at all. It is demonstrated that how the information from smartphone sensors can be used for positioning in an infrastructure free environment by means of a case study. An extension to the well-studied inertial navigation technique is implemented using smartphone mounted on a toy vehicle over an artificial testbed – Scalextric track. Having learnt that infrastructure free positioning is possible using only the inertial navigation sensors embedded in smartphone, off the shelf stride estimation methods (foot step detection techniques and stride length estimation models) are applied to investigate the most suitable stride estimation method for smartphone based pedestrian dead reckoning (PDR) positioning system. Unfortunately, what was most noticeable in all the methods was that their performance was user specific and importantly, dependent on heuristic parameters. In addition, the position error grows overtime because of slowly accumulating errors in the measurement of inertial sensor. To reduce the dependency on heuristic parameters we investigate the statistical approach – ‘Kalman filter’ to get a better estimate of the stride lengths. Nevertheless, drifts are mitigated by enforcing constraints from map using map matching technique – multiple uncertain routes engine (MURE). MURE is an extension to the Kalman filter that allows location to be described using multiple discrete Gaussian distributions bound to a map. The developed map aided pedestrian dead reckoning (PDR) system was field tested in different buildings. It yielded accurate matching results as well as a significant enhancement in positioning accuracy. Experimental results demonstrate that the mean absolute position error is less than 1.3 m and 95% confidence interval is between -3.16 m to 3.32 m. To further improvise the performance of map aided PDR system an extension to map based positioning is proposed via using landmarks. Landmark based positioning uses human as a sensor to sense proximity to landmarks. Landmarks are nothing specific as such but objects that are unique enough in comparison to the adjacent items e.g. quick response (QR) codes. Experimental results demonstrate that when map based positioning is used in addition to landmark based positioning the mean absolute position error is less than 1.0 m and 95% confidence interval is between -2.0 m to 2.0 m. Smartphones are mostly held in hands however these can be used as a lieu to dedicated wearable gadgets e.g. smart glasses that contain the similar set of sensors as smartphones. Hence, we investigate a scenario similar to smart glasses via smartphone mounted on helmet. The thesis concludes that in principle it is possible to logically localize a pedestrian within buildings using the inertial sensors embedded in smartphone. The algorithms developed in this thesis are suited to cases in which it is impossible or impractical to install large amounts of fixed infrastructure into the environment in advance. Also, methods proposed in this thesis are applicable in indoor tracking applications.

Los seres humanos pasan gran parte de sus vidas en ambientes interiores y aún no existe un sistema de posicionamiento que pueda ser desplegado en cualquier entorno interior del mundo y estimar con precisión la posición de las personas dentro de este entorno. Por esta razón, en la última década ha habido un creciente interés en la investigación de sistemas de posicionamiento en interiores. Esta tesis doctoral pretende contribuir al estudio de sistemas de posicionamiento en interiores con el diseño de nuevos sistemas desde un punto de vista experimental, es decir, pretendemos diseñar sistemas que puedan ser implementados utilizando las tecnologías comerciales actuales. En primer lugar, hemos considerado el diseño de un sistema de posicionamiento híbrido que combina las medidas inerciales de una unidad de medición inercial montada en la cadera del usuario con las medidas de potencia recibida de una red de sensores inalámbricos. En particular, diseñamos dos métodos para explotar la estadística de las medidas de potencia con el fin de extender en el tiempo la precisión a corto plazo de los sensores inerciales. Posteriormente, continuamos el estudio de sistemas de posicionamiento en interiores basados en redes de sensores teniendo en cuenta el uso de múltiples receptores. Desplegamos múltiples receptores en el cuerpo del usuario y aprovechamos las diferentes atenuaciones sufridas por el efecto del cuerpo humano sobre las señales inalámbricas para estimar la posición, la velocidad y el rumbo del usuario sin necesidad de utilizar sensores inerciales. Finalmente, con el objetivo de aplicar nuestros diseños a aplicaciones para un mercado a gran escala, nos trasladamos a una red WiFI y dispositivos comerciales, teléfonos inteligentes y relojes inteligentes. El teléfono coopera con el reloj con el fin de eludir los problemas producidos por el uso de redes WiFi de terceros. Específicamente, diseñamos un sistema híbrido de posicionamiento en interiores que combina las medidas inerciales de un teléfono inteligente colocado en el bolsillo del usuario con las medidas de potencia calculadas por el teléfono y el reloj.<br>Humans spend a large part of its lives in indoor environments and yet there is not a positioning system that can be deployed in any indoor environment of the world and accurately estimate the position of the people inside this environment. For this reason, in the last decade there has been an increasing interest in the research of indoor positioning systems. This PhD dissertation aims to contribute to the study of indoor positioning systems with the design of new systems from an experimental point of view, that is, we aim to design systems that can be implemented using nowadays commercial technologies. First, we have considered the design of an hybrid positioning system that combines the inertial measurements from a hip mounted inertial measurement unit with the RSS measurements from a wireless sensor network. Particularly, we design two methods for exploiting the statistics of the RSS measurements in order to extend in time the short term accuracy of the inertial sensors. Afterwards, we continue the study of indoor positioning systems based on WSN by extending the problem to the multiple receiver case. We deploy multiple receivers on the body of the user and take advantage of the different attenuations suffered due to the effect of the human body on the wireless signals in order to estimate the position, velocity and heading of the user without the need of using inertial sensors. Finally, with the aim of applying our designs to mass market applications, we move to a WiFI network and commercial devices, smartphones and smartwatches. The smartphone cooperates with the smartwatch in order to circumvent the problems produced for the use of third party WiFi networks. Specifically, we design an hybrid indoor positioning system that combines the inertial measurements from a smartphone placed in the pocket of the user with the RSS measurements received from the smartphone and the smartwatch.

The widespread use of mobile devices and the rise of Global Navigation Satellite Systems (GNSS) have allowed mobile tracking applications to become very popular and valuable in outdoor environments. However, tracking pedestrians in indoor environments with Global Positioning System (GPS)-based schemes is still very challenging given the lack of enough signals to locate the user. Along with indoor tracking, the ability to recognize pedestrian behavior and activities can lead to considerable growth in location-based applications including pervasive healthcare, leisure and guide services (such as, museum, airports, stores, etc.), and emergency services, among the most important ones. This thesis presents a system for pedestrian tracking and activity recognition in indoor environments using exclusively common off-the-shelf sensors embedded in smartphones (accelerometer, gyroscope, magnetometer and barometer). The proposed system combines the knowledge found in biomechanical patterns of the human body while accomplishing basic activities, such as walking or climbing stairs up and down, along with identifiable signatures that certain indoor locations (such as turns or elevators) introduce on sensing data. The system was implemented and tested on Android-based mobile phones with a fixed phone position. The system provides accurate step detection and count with an error of 3% in flat floor motion traces and 3.33% in stairs. The detection of user changes of direction and altitude are performed with 98.88% and 96.66% accuracy, respectively. In addition, the activity recognition module has an accuracy of 95%. The combination of modules leads to a total tracking error of 90.81% in common human motion indoor displacements.

Sensor Fusion is an essential framework in many Engineering fields. It is a relatively new paradigm for integrating data from multiple sources to synthesize new information that in general would not have been feasible from the individual parts. Within the wireless communications fields, many emerging technologies such asWireless Sensor Networks (WSN), the Internet of Things (IoT), and spectrum sharing schemes, depend on large numbers of distributed nodes working collaboratively and sharing information. In addition, there is a huge proliferation of smartphones in the world with a growing set of cheap powerful embedded sensors. Smartphone sensors can collectively monitor a diverse range of human activities and the surrounding environment far beyond the scale of what was possible before. Wireless communications open up great opportunities for the application of sensor fusion techniques at multiple levels. In this dissertation, we identify two key problems in wireless communications that can greatly benefit from sensor fusion algorithms: Automatic Modulation Classification (AMC) and indoor localization and mapping based on smartphone sensors. Automatic Modulation Classification is a key technology in Cognitive Radio (CR) networks, spectrum sharing, and wireless military applications. Although extensively researched, performance of signal classification at a single node is largely bounded by channel conditions which can easily be unreliable. Applying sensor fusion techniques to the signal classification problem within a network of distributed nodes is presented as a means to overcome the detrimental channel effects faced by single nodes and provide more reliable classification performance. Indoor localization and mapping has gained increasing interest in recent years. Currently-deployed positioning techniques, such as the widely successful Global Positioning System (GPS), are optimized for outdoor operation. Providing indoor location estimates with high accuracy up to the room or suite level is an ongoing challenge. Recently, smartphone sensors, specially accelerometers and gyroscopes, provided attractive solutions to the indoor localization problem through Pedestrian Dead-Reckoning (PDR) frameworks, although still suffering from several challenges. Sensor fusion algorithms can be applied to provide new and efficient solutions to the indoor localization problem at two different levels: fusion of measurements from different sensors in a smartphone, and fusion of measurements from several smartphones within a collaborative framework.<br>Ph. D.

This thesis investigates applicable indoor navigation systems for the next generation of hand-held shopping scanners, on behalf of the company Virtual Stores. The thesis research and review applicable indoor localization methods and ways to combine and evaluate received localization data in order to provide accurate navigation without introducing any other worn equipment for a potential user. Prototype navigation systems was proposed, developed and evaluated using a combination of carefully placed radio transmitters that was used to provide radio based localization methods using Bluetooth or UltraWide Band (UWB) and inertial sensors combined with a particle filter. The Bluetooth solution was deemed incapable of providing any accurate localization method while the prototype using a combination of UWB and inertial sensors proved promising solution with below 1m average error under optimal conditions or 2.0m average localization error in a more realistic environment. However, the system requires the surveyed area to provide 3 or more UWB transmitters in the line of sight of the UWB receiver of the user at every location facing any direction to provide accurate localization. The prototype also requires to be scaled up to provide localization to more than 1 radio transmitters at the time before being introduced to the Fast moving consumer goods market.<br>Denna avhandling undersöker tillämpliga inomhusnavigationssystem för nästa generations handhållna shopping terminaler, på uppdrag av företaget Virtual Stores. Avhandlingen undersöker och granskar tillämpliga inomhuslokaliseringsmetoder och sätt att kombinera och utvärdera mottagna lokaliseringsdata för att bistå med ackurat navigering utan att introducera någon ytterligare utrustning för en potentiell användare. Prototypnavigationssystem föreslogs, utvecklades och utvärderades användandes en kombination av väl utplacerade radiosändare användandes Bluetooth eller UltraWide Band (UWB) och tröghetssensorer i kombination med ett partikelfilter. Bluetooth-lösningen ansågs oförmögen att tillhandahålla någon exakt lokalisering medan prototypen som använde en kombination av UWB och tröghetssensorer visade sig vara en lovande lösnings med under 1m genomsnittligt fel under optimala förhållanden eller 2,0m genomsnittligt lokaliseringsfel i mer realistisk miljö. Systemet kräver emellertid att det undersökta området tillhandahåller 3 eller fler UWB-sändare inom synfältet för UWB-mottagaren hos användaren vid varje plats och riktning för att tillhandahålla ackurat lokalisering. Prototypen behöver skalas upp för att kunna bistå med lokalisering till mer än 1 radiomottagare innan den introduceras till detaljhandlen.

En réponse au nombre toujours élevé de décès provoqués par les accidents routiers, l'industrie automobile a fait de la sécurité un sujet majeur de son activité global. Les radars automobiles qui étaient de simples capteurs pour véhicule de confort, sont devenus des éléments essentiels de la norme de sécurité routière. Le domaine de l’automobile est un domaine très exigent en terme de sécurité et les radars automobiles doivent avoir des performances de détection très élevées et doivent répondre à des nombreuses contraintes telles que la facilité de production et/ou le faible coût. Cette thèse concerne le développement d’algorithmes pour la détection et la localisation de piétons et de cyclistes pour des radars automobiles de nouvelle génération. Nous avons proposé une architecture de réseau d'antennes non uniforme optimale et des méthodes d'estimation spectrale à haute résolution permettant d’estimer avec précision la position angulaire des objets à partir de la direction d'arrivée (DoA) de leur réponse. Ces techniques sont adaptées à l'architecture du réseau d'antennes proposé et les performances sont évaluées à l'aide de données radar automobiles simulées et réelles acquises dans le cadre de scénarios spécifiques. Nous avons également proposé un détecteur de cible de collision, basé sur la décomposition en sous-espaces Doppler, dont l'objectif principal est d'identifier des cibles latérales dont les caractéristiques de trajectoire représentent potentiellement un danger de collision. Une méthode de calcul d'attribut de cible est également développée et un algorithme de classification est proposé pour discriminer les piétons, cyclistes et véhicules. Les différents algorithmes sont évalués et validés à l'aide de données radar automobiles réelles sur plusieurs scenarios<br>In response to the persistently high number of deaths provoked by road crashes, the automotive industry has promoted safety as a major topic in their global activity. Automotive radars have been transformed from being simple sensors for comfort vehicle, to becoming essential elements of safety standard. The design of new generations automotive radars has to face various constraints and generally proposes a compromise between reliability, robustness, manufacturability, high-performance and low cost. The main objective of this PhD thesis is to design algorithms for the detection and localization of pedestrians and cyclists using new generation automotive radars. We propose an optimal non-uniform antenna array architecture and some high resolution spectral estimation methods to accurately estimate the position of objects from the direction of arrival (DOA) of their responses to the radar. These techniques are adapted to the proposed antenna array architecture and the performance is evaluated using both simulated and real automotive radar data, acquired in the frame of specific scenarios. We propose a collision target detector, based on the orthogonality of angle-Doppler subspaces, whose main goal is to identify lateral targets, whose trajectory features represent potentially a danger of collision. A target attribute calculation method is also developed and classification algorithm is proposed to classify pedestrian, cyclists and vehicles. This classification algorithm is evaluated and validated using real automotive radar data with several scenarios

Grazie al rapido avanzamento delle nuove tecnologie si è posta sempre più attenzione sui servizi di localizzazione. Tuttavia, data la natura fisica dei segnali emessi, i sistemi di geolocalizzazione tradizionali non riescono ad attraversare ostacoli come le mura degli edifici. StepApp è un sistema di localizzazione indoor, scalabile e a basso costo, che fonde la tecnica WiFi-Fingerprinting (basata sugli RSS provenienti dagli Access Point) e Pedestrian Dead Reckoning (basata sui sensori inerziali dello smartphone) al fine di migliorare l'accuratezza del posizionamento all'interno degli edifici. Lo scopo di StepApp è anche quello di fornire al suo interno tre differenti algoritmi di localizzazione tramite WiFi e quattro differenti modalità di localizzazione tramite sensori e in ultimo di inglobare cinque algoritmi di fusione delle due tecniche. Il sistema è stato testato in due differenti ambienti indoor con diverse caratteristiche e dall'analisi dei dati si sono registrati ottimi risultati in termini di accuratezza, raggiungendo una precisione media nella localizzazione superiore al 90% per quasi tutti gli algoritmi di fusione.