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The aircraft autopilot needs the feedback of airflow angles, that is, angle-of attack and sideslip angle to control the aircraft. The airflow angles measured by a mechanical vane are not accurate and very noisy for low airspeed. Moreover, the vane has a mechanical failure risk. This paper describes the extended Kalman filter based method to estimate the airflow angles and three-dimensional wind speed under constant wind condition. In addition, it can correct the scaling error of the airspeed of an aircraft. It uses the airspeed measurements, constant wind condition and the sideslip angle computed from GPS/INS navigation data and stability and control derivatives estimated from flight data. Simulation results show that the proposed method works well in various conditions. It is expected that estimated airflow angles will be able to replace the airflow angles measured by a primary system. Estimated wind speed can also be used to reconstruct the GPS/INS navigation system by correcting the airspeed in the case of GPS failure. [ABSTRACT FROM PUBLISHER]

Collision avoidance of UAV is an important function required from pilots and traffic controller as it intends to fly in the airspace of manned aircraft. It is implemented into Smart UAV which the flight test for automatic take-off, conversion and mission flight was conducted successfully last year. Prior to flight test for collision avoidance, the hardware-in-the-loop simulation (HILS) test is conducted in order to verify system operation and avoidance algorithm. Intruders are simulated through virtual aircraft models on the ground and transmitted to digital flight control computer (DFCC). The detection and avoidance algorithm is implemented to the operational flight program (OFP) in DFCC. This paper gives the description for the configuration of collision avoidance system, HILS system and the result for the simulation test in the case of multiple intruders. [ABSTRACT FROM PUBLISHER]

Most aircrafts are equipped with an air data system to measure airspeed, angle-of-sideslip(AOS) and so on. An air data system consists of Pitot-static tube and gimbal-mounted weathervane. The static pressure obtained by Pitot-static tube is affected by fuselage nose and angle-of-attack(AOA). To measure AOA and AOS accurately, weathervane should be placed out of boundary layer around fuselage nose. But due to structural problems, it is not easy to keep sufficient distance between static-hole of Pitot-static tube and fuselage nose. This gives rise to the error of measured air data and it is difficult to correct this error by wind-tunnel test or computational method. Thus, air data system must be calibrated through flight test after installation. This paper proposes the Kalman filter setup to calibrate static pressure, or airspeed and barometric altitude using GPS ground velocity. And also Kalman filter based AOS calibration method is proposed using flight data. Proposed methods are verified by simulation and real flight data of S m a r t U AV. [ABSTRACT FROM PUBLISHER]

Flight test results of flight control performance of Smart UAV were described. Flight test in airplane mode was performed by internal pilot using automatic functions including auto take-off and landing, speed/altitude/heading hold as well as a point navigation and a preprogram mode. It reached to the designed maximum speed at operational altitude. The rotor speed in airplane mode was reduced to 82% from 98% RPM in order to increase rotor efficiency with reducing Mach number at tip of rotors. Rotor governor was designed to maintain dual variable rotor speed in the flight control law. The performance of flight control including speed, altitude, heading error as well as damping and frequency were evaluated in all flight envelope in the airplane mode. These control laws were modified to meet the design requirement of Smart UAV. This paper presents the flight test result of airplane mode of Smart UAV. Detailed test plan and the flight control performance were also described. [ABSTRACT FROM PUBLISHER]