Leveraging True Altitude Data to Improve Static Pressure Calibration from Dynamic Manoeuvres

The paper presents a method for enhancing the static pressure calibration of a high-performance aircraft. Despite the pre-flight calibration using CFD and Wind Tunnel techniques, position errors are generally observed in the free stream parameters, which necessitate further calibration of air data sensors using flight test data. In the present research, the pressure coefficient is estimated as a time-varying parameter in the flight path reconstruction environment implemented using the Extended Kalman Filtering technique. Aircraft kinematic equations were used for the implementation of the state and measurement models, and flight test data from full flight sorties were used in the estimation process. An extensive validation of the on-board air data calibration tables was conducted. Mean values of the static pressure coefficient were updated using data from multiple sorties, each including computed mean errors from three independent sensors. A comparative analysis between the pre-existing and estimated static pressure coefficients was performed to identify specific flight regimes or manoeuvres where further refinement is required. Finally, the accuracy of the estimated true static pressure was validated by comparing the corresponding pressure altitude with radio altimeter readings at low altitudes, demonstrating strong agreement and validating the effectiveness of the proposed calibration refinement method.