Magnetic-field feature extraction for indoor location estimation

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.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