Intelligent acoustic rotor speed estimation for an autonomous helicopter

De Montfort University Open Research Archive

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dc.contributor.author Passow, Benjamin N. en
dc.contributor.author Gongora, Mario Augusto en
dc.contributor.author Hopgood, Adrian A. en
dc.contributor.author Smith, Sophy en
dc.date.accessioned 2012-08-23T14:23:44Z
dc.date.available 2012-08-23T14:23:44Z
dc.date.issued 2012-11
dc.identifier.citation Passow, B.N., Gongora, M.A., Hopgood, A.A. and Smith, S. (2011) Intelligent acoustic rotor speed estimation for an autonomous helicopter. Journal of Applied Soft Computing, 12, (11), pp. 3313–3324 en
dc.identifier.issn 1568-4946
dc.identifier.uri http://hdl.handle.net/2086/6925
dc.description.abstract Acoustic sensing to gather information about a machine can be highly beneficial, but processing the data can be difficult. In this work, a variety of methodologies have been studied to extract rotor speed information from the sound signature of an autonomous helicopter, with no a-priori knowledge of its underlying acoustic properties. The autonomous helicopter has two main rotors that are mostly identical. In order to identify the rotors’ speeds individually, a comparative evaluation has been made of learning methods with input selection, reduction and aggregation methods. The resulting estimators have been tested on unseen training data as well as in actual free-flight tests. The best results were found by using a genetic algorithm to identify the important frequency bands, a centroid method to aggregate the bands, and a neural-network estimator of the rotor speeds. This approach succeeded in estimating individual rotor speeds of an autonomous helicopter without being distracted by the other, mainly identical, rotor. These results were achieved using standard, low-cost hardware, and a learning approach that did not require any a-priori knowledge about the system's acoustic properties. en
dc.language.iso en en
dc.publisher Elsevier en
dc.subject acoustic sensing en
dc.subject feature selection en
dc.subject genetic algorithm en
dc.subject artificial neural network en
dc.subject adaptive network-based fuzzy inference system en
dc.subject helicopter en
dc.subject rotorspeeds en
dc.title Intelligent acoustic rotor speed estimation for an autonomous helicopter en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.1016/j.asoc.2012.05.022
dc.researchgroup Cyber Security Centre en
dc.researchgroup DIGITS en
dc.ref2014.selected 1367395509_0310680107244_11_2


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