In order to measure the position of an indoor unmanned aerial vehicle (UAV), A combined position estimate system is designed and tested in this thesis. Two sensors, an optical flow sensor (Px4flow) and a ultrasonic sensor (Marvelmind), are employed to provide the raw measurement data. The extended Kalman filter (EKF) is used as the main data fusion method and some algorithms are built based on this, including the attitude estimation, angular rate compensation and the position estimation. A single board computer (Raspberry Pi) is employed and plays a role as the central controller. Matlab, C language and Python are used to produce all the programs.