HMM-Based tracking of moving terminals in dense multipath indoor environments

This paper deals with the problem of radio localization of moving terminals (MTs) for indoor applications with mixed line-of sight/non-line-of-sight (LOS/NLOS) conditions. To reduce false localizations, a Bayesian approach is proposed to estimate the MT position. The tracking algorithm is based on a Hidden Markov Model (HMM) that permits to jointly track both the MT position and the sight condition. Numerical results show that the proposed HMM method improves the localization accuracy in LOS/NLOS indoor environments

Location Cheating: A Security Challenge to Location-based Social Network Services

Location-based mobile social network services such as foursquare and Gowalla have grown exponentially over the past several years. These location-based services utilize the geographical position to enrich user experiences in a variety of contexts, including location-based searching and location-based mobile advertising. To attract more users, the location-based mobile social network services provide real-world rewards to the user, when a user checks in at a certain venue or location. This gives incentives for users to cheat on their locations. In this report, we investigate the threat of location cheating attacks, find the root cause of the vulnerability, and outline the possible defending mechanisms. We use foursquare as an example to introduce a novel location cheating attack, which can easily pass the current location verification mechanism (e.g., cheater code of foursquare). We also crawl the foursquare website. By analyzing the crawled data, we show that automated large scale cheating is possible. Through this work, we aim to call attention to location cheating in mobile social network services and provide insights into the defending mechanisms.Comment: 10 pages, 8 figures, accepted by the 31st International Conference on Distributed Computing Systems (ICDCS 2011

Hidden Markov models for radio localization in mixed LOS/NLOS conditions

Abstract—This paper deals with the problem of radio localization of moving terminals (MTs) for indoor applications with mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions. To reduce false localizations, a grid-based Bayesian approach is proposed to jointly track the sequence of the positions and the sight conditions of the MT. This method is based on the assumption that both the MT position and the sight condition are Markov chains whose state is hidden in the received signals [hidden Markov model (HMM)]. The observations used for the HMM localization are obtained from the power-delay profile of the received signals. In ultrawideband (UWB) systems, the use of the whole power-delay profile, rather than the total power only, allows to reach higher localization accuracy, as the power-profile is a joint measurement of time of arrival and power. Numerical results show that the proposed HMM method improves the accuracy of localization with respect to conventional ranging methods, especially in mixed LOS/NLOS indoor environments. Index Terms—Bayesian estimation, hidden Markov models (HMM), mobile positioning, source localization, tracking algorithms

Construcción de mapas de cobertura para comunicaciones inalámbricas

Conocer ciertas características sobre cómo es la propagación de la señal en determinados entornos es de vital importancia para el uso efectivo de una red de comunicaciones inalámbrica. Dependiendo de la complejidad del medio podemos utilizar como guía uno o varios modelos de propagación, pudiéndose llegar a buenas aproximaciones sobre el comportamiento de la señal. Bien sea para desarrollar modelos (empíricos o deterministas) o validarlos, se requieren mediciones experimentales. En otros casos no se dispone de un modelo de propagación, por lo que la única opción radica en tomar mediciones prácticas. Cualquiera sea el caso, a través de la representación de estas mediciones en función de la posición obtenemos lo que se suele llamar un mapa de comunicaciones o mapa de cobertura. Situados en este contexto, en este trabajo se desarrollaron herramientas para la construcción de mapas de comunicaciones a gran escala y a pequeña escala. Pensando en una solución modular, se desarrollaron diversos módulos para el meta sistema operativo ROS y se implementaron en un vehículo real todoterreno, y en un robot Pioneer P3AT. Se realizaron pruebas en un ambiente de especial interés para el grupo RoPeRT (Robotics, Perception and Real Time) de la Universidad de Zaragoza: el túnel ferroviario de Somport, que conecta Francia con España. Se obtuvo un mapa de cobertura a gran escala de una sección de especial interés, de unos 2.5 km de largo con cambio de pendiente, y uno más detallado a menor escala de una sección de 1 Km, donde aparecen atenuaciones importantes. Se compararon los resultados con un modelo de propagación basado en “Ray Tracing” (trazado de rayos), desarrollado por Valenzuela (1993). Se obtuvieron similitudes como la existencia de un notable fading, pero a la vez diferencias que dan importancia a las mediciones realizadas, como la ubicación de este fading y diversas atenuaciones que no aparecen en las simulaciones. Se verificó la repetibilidad de estos fenómenos realizando diversos experimentos, inclusive en días diferentes, cuestión que no se ha sido tratada con importante énfasis en la literatura. También se encontró que, debido a variaciones transversales, aplicando una diversidad espacial muy superior a la de las tarjetas comerciales, podemos mejorar la calidad de señal en la mayoría del trayecto estudiado. Los resultados obtenidos pueden ser utilizados tanto para el despliegue óptimo de redes inalámbricas, hasta inclusive para el desarrollo de técnicas de navegación para equipos multi-robot manteniendo la comunicación

A bayesian approach to wireless location problems

Several approaches for indoor location estimation in wireless networks are proposed. We explore non-hierarchical and hierarchical Bayesian graphical models that use prior knowledge about physics of signal propagation, as well as different modifications of Bayesian bivariate spline models. The hierarchical Bayesian model that incorporates information about locations of access points achieves accuracy that is similar to other published models and algorithms, but by using prior knowledge, this model drastically reduces the requirement for training data when compared to existing approaches. Proposed Bayesian bivariate spline models for location surpass predictive accuracy of existing methods. It has been shown that different versions of this model, in combination with sampling/importance resampling and particle filter algorithms, are suitable for the real-time estimation and tracking of moving objects. It has been demonstrated that plug-in versions of the bivariate Bayesian spline model perform as good as the full Bayesian version. A combination of two Bayesian models to reduce the maximum predictive error is proposed. Models presented in this work utilize MCMC simulations in directed acyclic graphs (DAGs) to solve ill-posed problem of location estimation in wireless networks using only received signal strengths. Similar approaches may be applied to other ill-posed problems

Real-time WiFi localization of heterogeneous robot teams using an online random forest

In this paper we present a WiFi-based solution to the localization and mapping problem for teams of heterogeneous robots operating in unknown environments. By exploiting wireless signal strengths broadcast from access points, a robot with a large sensor payload creates a WiFi signal map that can then be shared and utilized for localization by sensor-deprived robots. In our approach, WiFi localization is cast as a classification problem. An online clustering algorithm processes incoming WiFi signals that are then incorporated into an online random forest (ORF). The algorithm’s robustness is increased by a Monte Carlo localization algorithm whose sensor model exploits the results of the ORF classification. The proposed algorithm is shown to run in real-time, allowing the robots to operate in completely unknown environments, where a priori information such as a blue-print or the access points’ location is unavailable. A comprehensive set of experiments not only compares our approach with other algorithms, but also validates the results across different scenarios covering both indoor and outdoor environments

Exploring Dual-Camera-Based Augmented Reality for Cultural Heritage Sites

Context: Augmented Reality (AR) provides a novel approach for presenting cultural heritage content. Recent advances in AR research and the uptake of powerful mobile devices means AR is a viable option for heritage institutions, but there are challenges that must be overcome before high-quality AR is commonplace. Aims: This project details the development of an AR “magic camera” system featuring novel dual-camera marker-based tracking, allowing users to take AR photos at outdoor heritage sites using a tablet computer. The aims of the project were to assess the feasibility of the tracking method, evaluate the usability of the AR system, and explore implications for the heritage sector. Method: A prototype system was developed. A user study was designed, where participants had to recreate reference images as closely as possible using an iPad and the AR system around the University grounds. Data, such as completion time and error rates, were collected for analysis. The images produced were rated for quality by three experts. Results: Participants responded positively to the system, and the new tracking method was used successfully. The usability study uncovered a number of issues, most of which are solvable in future software versions. However, some issues, such as difficulty orientating objects, rely on improving hardware and software before they can be fixed, but these problems did not affect the quality of the images produced. Participants completed each task more quickly after initial slowness, and while the system was frustrating for some, most found the experience enjoyable. Conclusion: The study successfully uncovered usability problems. The dual-camera tracking element was successful, but the marker-based element encountered lighting problems and high false-positive rates. Orientating objects using inertial sensors was not intuitive; more research in this area would be beneficial. The heritage sector must consider development, maintenance and training costs, and site modification issues

Doctor of Philosophy

dissertationLocation information of people is valuable for many applications including logistics, healthcare, security and smart facilities. This dissertation focuses on localization of people in wireless sensor networks using radio frequency (RF) signals, speci cally received signal strength (RSS) measurements. A static sensor network can make RSS measurements of the signal from a transmitting badge that a person wears in order to locate the badge. We call this kind of localization method radio device localization. Since the human body causes RSS changes between pairwise sensor nodes of a static network, we can also use RSS measurements from pairwise nodes of a network to locate people, even if they are not carrying any radio device. We call this device-free localization (DFL). The rst contribution of this dissertation is to radio device localization. The human body has a major e ect on the antenna gain pattern of the transmitting badge that the person is wearing, however, existing r

Smart hierarchical WiFi localization system for indoors

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014En los últimos años, el número de aplicaciones para smartphones y tablets ha crecido rápidamente. Muchas de estas aplicaciones hacen uso de las capacidades de localización de estos dispositivos. Para poder proporcionar su localización, es necesario identificar la posición del usuario de forma robusta y en tiempo real. Tradicionalmente, esta localización se ha realizado mediante el uso del GPS que proporciona posicionamiento preciso en exteriores. Desafortunadamente, su baja precisión en interiores imposibilita su uso. Para proporcionar localización en interiores se utilizan diferentes tecnologías. Entre ellas, la tecnología WiFi es una de las más usadas debido a sus importantes ventajas tales como la disponibilidad de puntos de acceso WiFi en la mayoría de edificios y que medir la señal WiFi no tiene coste, incluso en redes privadas. Desafortunadamente, también tiene algunas desventajas, ya que en interiores la señal es altamente dependiente de la estructura del edificio por lo que aparecen otros efectos no deseados, como el efecto multicamino o las variaciones de pequeña escala. Además, las redes WiFi están instaladas para maximizar la conectividad sin tener en cuenta su posible uso para localización, por lo que los entornos suelen estar altamente poblados de puntos de acceso, aumentando las interferencias co-canal, que causan variaciones en el nivel de señal recibido. El objetivo de esta tesis es la localización de dispositivos móviles en interiores utilizando como única información el nivel de señal recibido de los puntos de acceso existentes en el entorno. La meta final es desarrollar un sistema de localización WiFi para dispositivos móviles, que pueda ser utilizado en cualquier entorno y por cualquier dispositivo, en tiempo real. Para alcanzar este objetivo, se propone un sistema de localización jerárquico basado en clasificadores borrosos que realizará la localización en entornos descritos topológicamente. Este sistema proporcionará una localización robusta en diferentes escenarios, prestando especial atención a los entornos grandes. Para ello, el sistema diseñado crea una partición jerárquica del entorno usando K-Means. Después, el sistema de localización se entrena utilizando diferentes algoritmos de clasificación supervisada para localizar las nuevas medidas WiFi. Finalmente, se ha diseñado un sistema probabilístico para seguir la posición del dispositivo en movimiento utilizando un filtro Bayesiano. Este sistema se ha probado en un entorno real, con varias plantas, obteniendo un error medio total por debajo de los 3 metros