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    Spatial filtering of near-field radio frequency interference at a LOFAR LBA station

    Access Status
    Fulltext not available
    Authors
    Steeb, J.
    Davidson, David
    Wijnholds, S.
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Steeb, J. and Davidson, D. and Wijnholds, S. 2017. Spatial filtering of near-field radio frequency interference at a LOFAR LBA station, pp. 117-122.
    Source Title
    Proceedings of 2016 Radio Frequency Interference: Coexisting with Radio Frequency Interference, RFI 2016
    DOI
    10.1109/RFINT.2016.7833544
    ISBN
    9781509062010
    School
    Curtin Institute of Radio Astronomy (Engineering)
    URI
    http://hdl.handle.net/20.500.11937/72475
    Collection
    • Curtin Research Publications
    Abstract

    © 2016 IEEE. In preparation for the SKA, many new RFI (radio frequency interference) mitigation algorithms have been developed. However, these algorithms usually assume that the RFI source is in the far-field and that the array is calibrated. In this paper, the recovery of astronomical signals from uncalibrated RFI-corrupted LOFAR visibility data using spatial filtering methods are presented. For this demonstration, a near-field continuous-wave RFI source was generated by a hexacopter that was flown around one of the LOFAR LBA (low-band antenna) arrays. Four spatial filtering methods were applied to the RFI contaminated data: orthogonal projection, orthogonal projection with subspace bias correction, oblique projection and subspace subtraction. Overall, orthogonal projection with subspace bias correction performed the best, however it requires that the RFI source moves relative to the array and it is computationally expensive. Oblique projection performs similar to orthogonal projection with subspace bias correction when point sources are to be recovered and is furthermore considerably less computationally expensive. Subspace subtraction is a suitable alternative if a large field of view is to be recovered at a relatively low computational cost.

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