Advanced Pedestrian Positioning System to Smartphones and Smartwatches

In recent years, there has been an increasing interest in the development of pedestrian navigation systems for satellite-denied scenarios. The popularization of smartphones and smartwatches is an interesting opportunity for reducing the infrastructure cost of the positioning systems. Nowadays, smartphones include inertial sensors that can be used in pedestrian dead-reckoning (PDR) algorithms for the estimation of the user's position. Both smartphones and smartwatches include WiFi capabilities allowing the computation of the received signal strength (RSS). We develop a new method for the combination of RSS measurements from two different receivers using a Gaussian mixture model. We also analyze the implication of using a WiFi network designed for communication purposes in an indoor positioning system when the designer cannot control the network configuration. In this work, we design a hybrid positioning system that combines inertial measurements, from low-cost inertial sensors embedded in a smartphone, with RSS measurements through an extended Kalman filter. The system has been validated in a real scenario, and results show that our system improves the positioning accuracy of the PDR system thanks to the use of two WiFi receivers. The designed system obtains an accuracy up to 1.4 m in a scenario of 6000 m2

A Review of pedestrian indoor positioning systems for mass market applications

In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications

Autonomous car parking system through a cooperative vehicular positioning network

The increasing development of the automotive industry towards a fully autonomous car has motivated the design of new value-added services in Vehicular Sensor Networks (VSNs). Within the context of VSNs, the autonomous car, with an increasing number of on-board sensors, is a mobile node that exchanges sensed and state information within the VSN. Among all the value added services for VSNs, the design of new intelligent parking management architectures where the autonomous car will coexist with traditional cars is mandatory in order to profit from all the opportunities associated with the increasing intelligence of the new generation of cars. In this work, we design a new smart parking system on top of a VSN that takes into account the heterogeneity of cars and provides guidance to the best parking place for the autonomous car based on a collaborative approach that searches for the common good of all of them measured by the accessibility rate, which is the ratio of the free parking places accessible for an autonomous car. Then, we simulate a real parking lot and the results show that the performance of our system is close to the optimum considering different communication ranges and penetration rates for the autonomous car

Wearable-Based pedestrian localization through fusjon of inertial sensor measurements

Hoy en día existe una gran demanda de sistemas de navegación personales integrados en servicios como gestión de desastres para personal de rescate. También se demandan sistemas de navegación personales como guía en grandes superficies, por ejemplo, hospitales, aeropuertos o centros comerciales. En esta tesis doctoral los escenarios estudiados son interiores y urbanos. La navegación se realiza por medio de sensores inerciales y magnéticos, idóneos por su amplia difusión, tamaño y peso reducido y porque no necesitan infraestructura. Se llevarán a cabo investigaciones para mejorar los algoritmos de navegación ya existentes y cubrir determinados aspectos aún no resueltos. En primer lugar se ha llevado a cabo un extenso análisis sobre los beneficios de usar medidas magnéticas para compensar los errores sistemáticos de los sensores inerciales, así como su efecto en la estimación de la orientación. Para ello se han usado medidas de referencia con valores de error conocidos combinando diferentes distribuciones de campos magnéticos. Los resultados obtenidos quedan respaldados con medidas realizadas con sensores reales de medio coste. Se ha concluido que el uso de medidas magnéticas es beneficioso porque acota errores en la orientación. Sin embargo, los escenarios bajo estudio suelen presentar campos magnéticos perturbados, lo que provoca que el proceso de estimación de errores sea prohibitivamente largo. En esta tesis doctoral se proponen algoritmos alternativos para el cálculo del desplazamiento horizontal del usuario, que han sido comparados con respecto a los ya existentes, ofreciendo los propuestos un mejor rendimiento. Además se incluye un innovador algoritmo para calcular el desplazamiento vertical del usuario, haciendo por primera vez posible obtener trayectorias en 3D usando solamente sensores inerciales no colocados en el zapato. Por último se propone un novedoso algoritmo capaz de prevenir errores de posición provocados por errores de rumbo. El algoritmo está basado en puntos de referencia automáticamente detectados por medio de medidas inerciales. Los puntos de referencia elegidos para los escenarios cubiertos son escaleras y esquinas, que al revisitarse permiten calcular el error acumulado en la trayectoria. Este error es compensado consiguiendo así acotar el error de rumbo. Este algoritmo ha sido extensamente probado con medidas de referencia y medidas realizadas con sensores reales de medio coste. La compensación de este error se adapta a las características del sistema de navegación personal

Advanced Pedestrian Positioning System to Smartphones and Smartwatches

In recent years, there has been an increasing interest in the development of pedestrian navigation systems for satellite-denied scenarios. The popularization of smartphones and smartwatches is an interesting opportunity for reducing the infrastructure cost of the positioning systems. Nowadays, smartphones include inertial sensors that can be used in pedestrian dead-reckoning (PDR) algorithms for the estimation of the user's position. Both smartphones and smartwatches include WiFi capabilities allowing the computation of the received signal strength (RSS). We develop a new method for the combination of RSS measurements from two different receivers using a Gaussian mixture model. We also analyze the implication of using a WiFi network designed for communication purposes in an indoor positioning system when the designer cannot control the network configuration. In this work, we design a hybrid positioning system that combines inertial measurements, from low-cost inertial sensors embedded in a smartphone, with RSS measurements through an extended Kalman filter. The system has been validated in a real scenario, and results show that our system improves the positioning accuracy of the PDR system thanks to the use of two WiFi receivers. The designed system obtains an accuracy up to 1.4 m in a scenario of 6000 m2

Advanced Pedestrian Positioning System to Smartphones and Smartwatches

In recent years, there has been an increasing interest in the development of pedestrian navigation systems for satellite-denied scenarios. The popularization of smartphones and smartwatches is an interesting opportunity for reducing the infrastructure cost of the positioning systems. Nowadays, smartphones include inertial sensors that can be used in pedestrian dead-reckoning (PDR) algorithms for the estimation of the user’s position. Both smartphones and smartwatches include WiFi capabilities allowing the computation of the received signal strength (RSS). We develop a new method for the combination of RSS measurements from two different receivers using a Gaussian mixture model. We also analyze the implication of using a WiFi network designed for communication purposes in an indoor positioning system when the designer cannot control the network configuration. In this work, we design a hybrid positioning system that combines inertial measurements, from low-cost inertial sensors embedded in a smartphone, with RSS measurements through an extended Kalman filter. The system has been validated in a real scenario, and results show that our system improves the positioning accuracy of the PDR system thanks to the use of two WiFi receivers. The designed system obtains an accuracy up to 1.4 m in a scenario of 6000 m 2