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The primary scope of this study lays on system technique solutions of collecting data required for the identication of an aircraft’s nonlinear dynamic model. It is assumed that the aircraft has no inbuilt navigational system, nor any sensors mounted on its control surfaces. The control column and pedals manipulated by the pilot can only visually be observed. For the time of data logging, an external sensory system (GPS, IMU) and a camera system were deployed on the airplane supporting the collection of ight data. The paper presents the data acquisition solutions required for aircraft’s nonlinear model identication, with an emphasis on the determination of the control surface positions as the system’s input signals using image processing. During ight, the control column and pedal positions manipulated by the pilot are recorded using a video camera and with post processing, data is converted to control surface (rudder, elevator, aileron) positions. The 3D positions of the pilot’s control column are determined from 2D pixel values. The input signals are then calculated using this information and the control surface characteristics. The input signals and state variables determined with a state estimator are regarded as input signals for the identication of an aircraft’s nonlinear model.

Data fusion and primary image processing for aircraft identification