ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / Telemetry data are usually collected for analysis at some later time and can be monitored to follow the progress of a test. In the case of an Air Data System the signals from the sensors are sent to a computer that calculates the air data parameters for use on multiple LabView-generated displays, as well as to the Data Acquisition System. The readouts on the multiple displays need to be real-time so they are useful to the flight crew. Equations that control the different air data values are determined by what telemetry data are available and the preference of those doing the test planning. These systems need to display the information in a format useful to the flight crew and be reliable.

This thesis presents the calibration processes of the pitot-static system, which is a part of the air data system of a military transport aircraft through flight tests. Tower fly-by method is used for air data system calibration. Altitude error caused by the position of the static port on the aircraft is determined by analyzing the data collected during four sorties with different weight, flap and landing gear configurations. The same data has been used to determine the airspeed measurement error. It has been shown that both the altitude and airspeed errors are within the allowable limits specified by FAR 25. Same method is also used for trailing cone calibration that is used for high altitude test flights for RVSM certification.

This thesis presents an estimator to assist or replace a fighter aircraft’s air datasystem (ADS). The estimator is based on machine learning and LSTM neuralnetworks and uses the statistical correlation between states to estimate the angleof attack, angle of sideslip and Mach number using only the internal sensorsof the aircraft. The model is trained and extensively tested on a fighter jetsimulation model and shows promising results. The methodology and accuracyof the estimator are discussed, together with how a real-world implementationwould work. The estimators presented should act as a proof of concept of thepower of neural networks in state estimation, whilst the report discusses theirstrengths and weaknesses. The estimators can estimate the three targets wellin a vast envelope of altitudes, speeds, winds and manoeuvres. However, thetechnology is quite far from real-world implementation as it lacks transparencybut shows promising potential for future development. / Det här examensarbetet presenterar en estimator för att hjälpa eller ersätta ettstridsflygplans luftdatasystem (ADS). Estimatorn är baserad på maskininlärningoch LSTM neurala nätverk och använder statistisk korrelation mellan tillstånd föratt uppskatta anfallsvinkeln, sidglidningsvinkel och Mach-tal endast med hjälpav flygplanets interna sensorer. Modellen är tränad och utförligt testad på ensimuleringsmodell för stridsflygplan och visar lovande resultat. Estimatornsmetodik och noggrannhet diskuteras, tillsammans med hur en implementeringi verkligheten skulle fungera. De presenterade estimatorerna bör fungera somett “proof of concept” för kraften hos neurala nätverk för tillståndsuppskattning,medan rapporten diskuterar deras styrkor och svagheter. Estimatorerna kanuppskatta de tre tillstånden väl i ett stort spektra av altituder, hastigheter, vindaroch manövrar. Tekniken är dock ganska långt ifrån en verklig implementeringeftersom den saknar transparens, men visar lovande potential för framtidautveckling.