| dc.contributor.author | Mosiane, Olorato | |
| dc.contributor.author | Oozeer, Nadeem | |
| dc.contributor.author | Bassett, Bruce A. | |
| dc.date.accessioned | 2018-05-28T10:44:50Z | |
| dc.date.available | 2018-05-28T10:44:50Z | |
| dc.date.issued | 2017 | |
| dc.identifier.citation | Mosiane, O. et al. 2017. Machine learning for radio frequency interference mitigation using polarization. IEEE Radio and Antenna Days of the Indian Ocean 2017 (RADIO 2017), 25-28 Sep, Cape Town, South Africa. [https://doi.org/10.23919/RADIO.2017.8242211] | en_US |
| dc.identifier.issn | 1045-9243 | |
| dc.identifier.uri | http://hdl.handle.net/10394/26913 | |
| dc.identifier.uri | https://doi.org/10.23919/RADIO.2017.8242211 | |
| dc.identifier.uri | https://ieeexplore.ieee.org/document/8242211/ | |
| dc.description.abstract | Radio frequency interference (RFI) is electromagnetic
interference (EMI) from signals in the radio frequencies of
the electromagnetic spectrum. RFI reduces the sensitivity of radio
telescope and produces artefacts in the observed data. We present
the result of applying machine learning techniques to detect
confidently man made RFI. We confirm that not all the features
selected to characterise RFI are always important. We further
investigated the Random Forest Classifier (RFC) to characterize
RFI and conclude by showing that features having both polarized
information are more useful as compared to features carrying
single polarization information | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.subject | Training | |
| dc.subject | Data models | |
| dc.subject | Radiofrequency interference | |
| dc.subject | Feature extraction | |
| dc.subject | Radio astronomy | |
| dc.subject | Testing | |
| dc.subject | Roads | |
| dc.title | Machine learning for radio frequency interference mitigation using polarization | en_US |
| dc.type | Presentation | en_US |
| dc.contributor.researchID | 24287717 - Oozeer, Nadeem | |
