Indoor Position Estimation Using Ultrasonic Beacon Sensors and Extended Kalman Filter

Abstract

With the invention of GPS and related technologies, outdoor positional systems have become very accurate. However, there is still a need for efficient, reliable, and less expensive technology for indoor navigation. There are lots of techniques used for indoor navigation, such as acoustic, Wi-Fi-based, proximity-based, infrared systems and SLAM algorithms. In this study, accurate position estimation was attempted by combining the acceleration and gyroscope data and the raw distance data with the help of the Extended Kalman Filter (EKF). Initially, a position estimation was obtained using the Recursive Least Square (RLS) method with a trilateration algorithm. This solution was used as a starting point for RLS. Here, the first solution point is updated as the initial solution for each distance, and the result calculated by the RLS method is updated as the next solution. This approach enables the distance measurement and position estimation to be executed simultaneously, avoids the unnecessary waiting time, and speeds up the positioning estimation. After that, this position estimation is fused with the acceleration and gyroscope data. In order to test the designed algorithm, synthetic data were used. As a result of these tests, it has been observed that this EKF structure created for indoor navigation gives accurate results.