With the invention of GPS and related technologies outdoor positional system is possible with great accuracy. However, there is still a need for efficient, reliable and less expensive technology for indoor navigation. There are lots of techniques which are used for indoor navigation such as acoustic, wifi-based, proximity-based,infraded systems and SLAM algorithms. In this study, it was tried to obtain an accurate position estimation by combining the acceleration and gyroscope data obtained from the Marvelmind Beacon sensor, one of the ultrasonic sensors, and the raw distance data with the help of the Extended Kalman Filter (EKF). Initially, a position estimation is obtained using the Recursive Least Square(RLS) method with a trilateration algorithm. This solution is used as a starting point for RLS. Here, the first solution point is updated as the initial solution for each distance data, 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 and it avoids the unnecessary waiting time and speeds up the positioning estimation. After that, this position estimation is fused with the acceleration and gyroscope data which collected from the Marvelmind sensor. In this respect, this designed algorithm is similar to the tightly coupled EKF structure that produces its own GPS solution. In order to test the designed algorithm, data was collected from the Marvelmind sensor. While collecting these data, four stable and one moving Marvelmind sensor were used. Data collection was carried out via the serial port on the sensors with the help of the ROS platform. The collected data was first obtained in txt format and then converted to MATLAB format. The tests of the designed algorithm were carried out with these data. As a result of the tests, it has been observed that, this tightly coupled indoor EKF structure created for indoor navigation gives more accurate results.