Air data position-error calibration using state reconstruction techniques

Abstract

During the highly maneuverable aircraft technology (HiMAT) flight test program recently completed at NASA Ames Research Center's Dryden Flight Research Facility, numerous problems were experienced in airspeed calibration. This necessitated the use of state reconstruction techniques to arrive at a position-error calibration. For the HiMAT aircraft, most of the calibration effort was expended on flights in which the air data pressure transducers were not performing accurately. Following discovery of this problem, the air data transducers of both aircraft were wrapped in heater blankets to correct the problem. Additional calibration flights were performed, and from the resulting data a satisfactory position-error calibration was obtained. This calibration and data obtained before installation of the heater blankets were used to develop an alternate calibration method. The alternate approach took advantage of high-quality inertial data that was readily available. A linearized Kalman filter (LKF) was used to reconstruct the aircraft's wind-relative trajectory; the trajectory was then used to separate transducer measurement errors from the aircraft position error. This calibration method is accurate and inexpensive. The LKF technique has an inherent advantage of requiring that no flight maneuvers be specially designed for airspeed calibrations. It is of particular use when the measurements of the wind-relative quantities are suspected to have transducer-related errors