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dc.contributor.authorMosiane, Olorato
dc.contributor.authorOozeer, Nadeem
dc.contributor.authorBassett, Bruce A.
dc.identifier.citationMosiane, O. et al. 2016. Radio frequency interference detection using machine learning. IEEE Radio and Antenna Days of the Indian Ocean (RADIO), 10-13 Oct. []en_US
dc.identifier.isbn978-1-5090-2580-0 (Online)
dc.description.abstractRadio frequency interference (RFI) has plagued radio astronomy which potentially might be as bad or worse by the time the Square Kilometre Array (SKA) comes up. RFI can be either internal (generated by instruments) or external that originates from intentional or unintentional radio emission generated by man. With the huge amount of data that will be available with up coming radio telescopes, a machine learning technique will be required to detect RFI. In this paper we present the result of applying such machine learning techniques to cross match RFI from the Karoo Array Telescope (KAT-7) data. We found that not all the features selected to characterise RFI are always important. We further investigated 3 machine learning techniques and conclude that the Random forest classifier performs with a 98% Area Under Curve and 91% recall in detecting RFIen_US
dc.subjectFeature extractionen_US
dc.subjectRadio astronomyen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectRadiofrequency interferenceen_US
dc.subjectData miningen_US
dc.subjectTime series analysisen_US
dc.titleRadio frequency interference detection using machine learningen_US
dc.contributor.researchID24287717 - Oozeer, Nadeem

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