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    Statistical classification of radio frequency interference (RFI) in a radio astronomy environment

    Access Status
    Fulltext not available
    Authors
    Wolfaardt, C.
    Davidson, David
    Niesler, T.
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Wolfaardt, C. and Davidson, D. and Niesler, T. 2017. Statistical classification of radio frequency interference (RFI) in a radio astronomy environment.
    Source Title
    2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, PRASA-RobMech 2016
    DOI
    10.1109/RoboMech.2016.7813164
    ISBN
    9781509033355
    School
    Curtin Institute of Radio Astronomy (Engineering)
    URI
    http://hdl.handle.net/20.500.11937/73036
    Collection
    • Curtin Research Publications
    Abstract

    © 2016 IEEE. We present the application of statistical classifiers to the problem of automatic identification of radio frequency interference (RFI) in radio astronomy. RFI can corrupt measurements made by radio telescopes and it is therefore very important that such interference can be identified. We compile a dataset of RFI signals gathered at the SKA site near Carnavon, South Africa, and use this data to train and evaluate some statistical classifiers. We find the best performing system to use the k-nearest-neighbour (knn) classifier and achieve an accuracy of 93%. Since our dataset was limited by the capturing equipment in terms of record length, we feel that there is scope to improve on this figure in the future.

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