Résumé:
In today's world, the demand for reliable and efficient localization systems has become more important. Traditional systems like GPS often fall short in complex environments such as buildings or conflict zones, where reliable positioning can be challenging. This limitation highlights the need for precise, cost-effective localization solutions that can function seamlessly in various scenarios. To address this gap, we propose a proprietary indoor positioning system based on cell phone tower signals and located devices, offering an alternative that is both reliable and accessible.
Our system uses machine learning and deep learning techniques to process data col- lected from various mobile devices. By analyzing signals from cellular towers, it can accurately predict the location of users. This method enhances location accuracy in dif- ferent environments while providing an alternative to traditional GPS systems. Our work addresses the growing demand for reliable location solutions.
Keywords: localization systems, indoor positioning, machine learning, deep learning, cellular.
