RaFIDe: A Machine Learning based RFI free observation planner for the SKA Era

S.S., Bhat; T, Prabu; S, Saha

Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/7804

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DC Field	Value	Language
dc.contributor.author	S.S., Bhat	-
dc.contributor.author	T, Prabu	-
dc.contributor.author	S, Saha	-
dc.date.accessioned	2021-07-27T06:24:51Z	-
dc.date.available	2021-07-27T06:24:51Z	-
dc.date.issued	2020-09	-
dc.identifier.citation	33rd General Assembly and Scientific Symposium of the International-Union-of-Radio-Science	en_US
dc.identifier.uri	http://hdl.handle.net/2289/7804	-
dc.description	Restricted Access	en_US
dc.description.abstract	Signal anomalies in astronomical data mainly come from Radio Frequency Interference (RFI). Radio Frequency Interference (RFI) has plagued the field of radio astronomy. RFI can be either internal (generated by instruments) or external that originates from intentional or unintentional radio emission generated by human activity. Radio Telescopes are known to generate massive amounts of astronomical data. With the huge amount of data being available, a clustering technique can be applied to detect RFI. The quality of the incoming radio signal will be determined by the clustering technique. This will enable us to detect the anomalies in the signal at a particular instant of time. This effort will further enable us to build a database and subsequently apply reinforced time-series machine learning models to predict the quality of the signal. This paper proposes a machine learning approach to study the signal quality over the recent past and make use of this knowledge to plan the near-future observation slots in frequency spectrum and time.	en_US
dc.language.iso	en	en_US
dc.publisher	IEEE	en_US
dc.rights	2020 IEEE	en_US
dc.title	RaFIDe: A Machine Learning based RFI free observation planner for the SKA Era	en_US
dc.type	Article	en_US
Appears in Collections:	Research Papers(EEG)

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