Enhancing Indoor Position Estimation Accuracy: Integration of IMU, Raw Distance Data, and Extended Kalman Filter with Comparison to Vicon Indoor Positioning System Data

In today's world, indoor positioning systems that facilitate people's navigation inside buildings have become a significant area of research and development. These systems offer effective solutions by combining different technologies to determine users' locations in indoor environments where GPS signals are not accessible. Indoor positioning systems are not only beneficial for easing navigation in places like shopping malls, airports, and hospitals but also draw attention for their potential to optimize evacuation processes during emergencies. The aim of this study is to examine various technologies and algorithms used in indoor positioning systems and evaluate their suitability for different application areas. The fundamental technologies used for indoor positioning include Bluetooth Low Energy (BLE), Wi-Fi, magnetic field detection, ultra-wideband (UWB), and sensor fusion methods. In this study, research has been conducted on raw distance data and different Kalman filters to achieve a more accurate indoor position estimation. For initial position estimation, a trilateration algorithm based on Recursive Least Squares (RLS) has been employed using distance data. Subsequently, the outputs of this trilateration algorithm have been fused with accelerometer and gyroscope data. During this fusion process, both Extended Kalman Filter (EKF) and Cubature Kalman Filter (CKF) algorithms have been utilized. The obtained results have facilitated a comparison between these two algorithms. The data used for algorithm testing has been acquired from real sensor data. Based on the test results, the two algorithms have been compared using Root Mean Square Error (RMSE) and process time metrics.