Unmanned air vehicle air data estimation using a matrix of pressure sensors: a comparison of neural networks and look-up tables

Abstract

Flush airdata sensing (FADS) systems are cost- and weight- effective alternatives to current air data booms for measuring important air data parameters such as airspeed, angle of attack, sideslip, etc. Most applications consider large manned/unmanned air vehicles where the Pitot-static tube is located at the nose tip. However, traditional air data booms can be physically impractical for micro- (unmanned) air vehicles (MAVs) and, in this article, a FADS system mounted on the wing leading edge of a MAV flown at low speeds of Mach 0.07 (wind tunnel experiments under corresponding conditions) is designed. Moreover, two approaches for converting the FADS system pressure to meaningful air data are compared: a neural network (NN) approach and a look-up table (LUT). Results have shown that instrumentation weight and cost were reduced by 80 per cent and 97 per cent, respectively, in comparison to a traditional air data boom. Overall, the NN estimation accuracies were 0.51°, 0.44 lb/ft², and 0.62 m/s and the LUT estimation accuracies 1.32°, 0.11 lb/ft², and 0.88 m/s for the angle of attack, static pressure, and airspeed, respectively. It was also found that the LUT has faster execution times while the NN was in most cases more robust to sensor faults. However, while the LUT requires high memory usage, especially for higher dimensions, the NN can be executed in a few lines of code.