Resumen:
User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the
modern mobile devices. These sensory devices allow us to exploit not only
infrastructures made for every day use, such as Wi-Fi, but also natural
infrastructure, as is the case of natural magnetic fields. From our experience working with mobile devices and Magnetic-Field based location
systems, we identify some issues that should be addressed to improve
the performance of a Magnetic-Field based system, such as a reduction
of the data to be analyzed to estimate an individual location. In this
paper we propose a feature extraction process that uses magnetic-field
temporal and spectral features to acquire a classification model using the
capabilities of mobile phones. Finally, we present a comparison against
well known spectral classification algorithms with the aim to ensure the
reliability of the feature extraction process.
Descripción:
User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices. These sensory devices allow us to exploit not only
infrastructures made for every day use, such as Wi-Fi, but also natural
infrastructure, as is the case of natural magnetic fields. From our experience working with mobile devices and Magnetic-Field based location
systems, we identify some issues that should be addressed to improve
the performance of a Magnetic-Field based system, such as a reduction
of the data to be analyzed to estimate an individual location. In this
paper we propose a feature extraction process that uses magnetic-field
temporal and spectral features to acquire a classification model using the
capabilities of mobile phones. Finally, we present a comparison against
well known spectral classification algorithms with the aim to ensure the
reliability of the feature extraction process.
