For most air vehicles, knowing the wind relative flight state (altitude, air velocity, angles of attack and sideslip) is necessary for safe flight operation. The wind relative air data state becomes critically important for gliding vehicles in order to ensure the vehicle has the proper glide range to reach the landing site. Traditionally, the wind relative vehicle state has been determined by using pitot probes and directional flow vanes. These instruments work well for speeds up to Mach 3, but tend to be "burned off' by high dynamic pressures and temperatures much beyond that point. The solution is to use a matrix of pressure ports mounted flush to the vehicle nosecap and an algorithm to compute the air data state from the measured pressures. This is known as a flush air data system. Flush air data systems have been in development since the 1980's, but have not been implemented for their ideal application of suborbital reentry vehicles because of unacceptable amounts of noise in the solvers at high altitudes. This is typically above 80,000 feet, although it depends on the pressure transducer. The source of this noise is primarily random signal noise in the sensors and bias errors distributed amongst the sensor ensemble, which play a greater role as atmospheric pressure drops and there effectively becomes nothing left to measure. However, the bias can be averaged out by increasing the number of sensors, and the noise tends to be symmetrically distributed about the true value and can be largely removed by using an inertial enhancement filter to blend in inertial data. This results in a far more stable algorithm output than exists for previously developed systems, and yields useful and accurate data through the entire flight path from launch through reentry to landing. While the algorithm can be largely generic and applied to virtually any vehicle, it must be calibrated to the airframe to be able to account for the effects of upwash and sidewash. The coefficients for this calibration can be determined either from wind tunnel testing, or from computational fluid dynamics modeling.
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