dc.contributor.author | Borup, Kasper Trolle | |
dc.contributor.author | Fossen, Thor I. | |
dc.contributor.author | Johansen, Tor Arne | |
dc.date.accessioned | 2019-11-25T10:11:44Z | |
dc.date.available | 2019-11-25T10:11:44Z | |
dc.date.created | 2019-11-23T15:13:25Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 0018-9251 | |
dc.identifier.uri | http://hdl.handle.net/11250/2630199 | |
dc.description.abstract | This paper presents a method for estimating the air data parameters for a small fixed-wing, unmanned aerial vehicle (UAV) using an arrangement of low-cost MEMS-based pressure sensors embedded in the surface of the UAV. The pressure measurements are used in a machine learning (ML) model to estimate the angle of attack (AOA), sideslip angle (SSA), and airspeed. Two ML algorithms based on artificial neural networks (NNs) and linear regression (LR) are implemented, tested, and assessed using data collected from wind tunnel experiments and a flight test and the results are compared to a benchmark flight test. Training the ML algorithms using wind tunnel data was found to introduce several potential error sources that need to be addressed in order to provide accurate estimation on the benchmark flight test, whereas training the algorithms using flight data provides lower estimation RMSE values. The performance of the NN structures has been found to slightly outperform the linear regression algorithms in estimation accuracy. Lastly, results from using different sensor configurations and a pseudo Reynolds number are presented in an effort to evaluate the influence of sensor number and placement on the accuracy of the method. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | nb_NO |
dc.title | A Machine Learning Approach for Estimating Air Data Parameters | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.journal | IEEE Transactions on Aerospace and Electronic Systems | nb_NO |
dc.identifier.doi | 10.1109/TAES.2019.2945383 | |
dc.identifier.cristin | 1751347 | |
dc.relation.project | Norges forskningsråd: 221666 | nb_NO |
dc.relation.project | Norges forskningsråd: 223254 | nb_NO |
dc.description.localcode | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | nb_NO |
cristin.unitcode | 194,63,25,0 | |
cristin.unitname | Institutt for teknisk kybernetikk | |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |