Complete Online Algorithm for Air Data System Calibration

Abstract

Air data systems require costly calibration of their static pressure sensors to characterize errors caused by the act of flying. Altitude-based methods for measuring these so-called static position errors, such as the tower flyby, produce accurate results but require an elaborate flyby site, multiple experiments to capture the relationship between error and airspeed, and are limited to subsonic airspeeds due to inherent hazards to land-based and aircraft structures from low-altitude supersonic flight. Airspeed-based methods using the Global Positioning System (GPS) are generally easier to execute, but they tend to yield less precise results and still require multiple experiments. Additionally, they require temperature probe calibration from external sources. This paper proposes a self-contained online method for complete air data calibration. The proposed method uses a Kalman smoother to fuse GPS altitude and airspeed measurements, aircraft attitude, and air data to produce the full static position error curve as a function of Mach number in a single experiment, with no need for external temperature calibration and with no supersonic limitations. The proposed method is validated using T-38C flight data, and it is shown to reduce cost by 88% while modeling a 42% larger domain when compared to current methods.