Machine learning approach to radio frequency interference(RFI) classification in Radio Astronomy

Wolfaardt, Cornelis Johannes (2016-03)

Thesis (MEng)--Stellenbosch University, 2016.

Thesis

ENGLISH ABSTRACT: Radio frequency interference (RFI) presents a large problem for radio telescopes. Interference prevents observations from being made, or extends the duration required for observations. This thesis investigates different methods to automatically classify RFI signals. Data from different sources was cap- tured at the SKA site. Both Gaussian Mixture Model (GMM) and K-nearest neighbors (KNN) classifiers were used to analyse the data. Both performed adequately, with the KNN slightly outperforming the GMM. Different feature extraction methods were also investigated.

AFRIKAANSE OPSOMMING: Radio frekwensie steurseine verteenwoordig `n groot probleem vir radio tele- skope. Steurseine verhoed teleskope om waarnemings te maak. Hierdie tesis ondersoek verskeie metodes om steurseine automaties te identifiseer en klasi- fiseer. Data van bekende steurseine op die SKA terrein is versamel. Verkeie voorverwerkingtegnieke word ondersoek en dan geannaliseer met bekende sta- tistiese modelle soos `n GMM en KNN. Beide lewer aanvaarbare resultate. Verskeie metodes om kenmerke te onttrek word ook ondersoek.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/98464
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