Fusion Beacon and Machine Vision Based on Extended Kalman Filter for Indoor Localization

Abstract

The advancements and evaluations of wireless devices, telecommunications infrastructure for wireless communications, as well as satellite networks have led to the development of many positioning systems for moving users, especially in an open environment. Unfortunately, many of these systems do not perform well indoors, and other solutions are needed for these environments. This study examines a sample of techniques and rules of indoor localization using radio signals and machine vision and attempts to use a combination method of positions obtained by a new technology called low-power Bluetooth, as well as video taken by the camera set in the environment to improve the results of this type of localization. The development of this technology depends on how much it is supported by today's smart devices such as smartphones, tablets, etc. Then, by analyzing the collected information, including signal and video from indoor environments, and also via the implementation of Kalman filter developed on this information, a better estimate of the user's localization has been reached.