{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T21:49:37Z","timestamp":1768772977740,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper puts forward a novel design for an airspeed instrument aimed at small fixed-wing tail-sitter unmanned aerial vehicles. The working principle is to relate the power spectra of the wall-pressure fluctuations beneath the turbulent boundary layer present over the vehicle\u2019s body in flight to its airspeed. The instrument consists of two microphones; one flush-mounted on the vehicle\u2019s nose cone, which captures the pseudo-sound caused by the turbulent boundary layer, and a micro-controller that processes the signals and computes the airspeed. A feed-forward single-layer neural network is used to predict the airspeed based on the power spectra of the microphones\u2019 signals. The neural network is trained using data obtained from wind tunnel and flight experiments. Several neural networks were trained and validated using only flight data, with the best one achieving a mean approximation error of 0.043 m\/s and having a standard deviation of 1.039 m\/s. The angle of attack has a significant impact on the measurement, but if the angle of attack is known, the airspeed could still be successfully predicted for a wide range of angles of attack.<\/jats:p>","DOI":"10.3390\/s23052463","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T03:56:58Z","timestamp":1677124618000},"page":"2463","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Microphones as Airspeed Sensors for Unmanned Aerial Vehicles"],"prefix":"10.3390","volume":"23","author":[{"given":"Momchil","family":"Makaveev","sequence":"first","affiliation":[{"name":"Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mirjam","family":"Snellen","sequence":"additional","affiliation":[{"name":"Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0060-6526","authenticated-orcid":false,"given":"Ewoud J. J.","family":"Smeur","sequence":"additional","affiliation":[{"name":"Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"ref_1","unstructured":"Hollister-Short, G., and James, F. (1998). History of Technology, Bloomsbury Publishing. [18th ed.]."},{"key":"ref_2","unstructured":"Anderson, J. (2016). Fundamentals of Aerodynamics, McGraw-Hill Education."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12005","DOI":"10.1088\/1742-6596\/648\/1\/012005","article-title":"Impact of Pitot tube calibration on the uncertainty of water flow rate measurement","volume":"648","author":"Barsaglini","year":"2015","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_4","unstructured":"Verling, S.L., Stastny, T., and Siegwart, R. (19\u201321, January 11\u201315). 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