Title: A Dictionary Approach to Identifying Transient RFI,
Abstract: As radio telescopes become more sensitive, the damaging effects of radio
frequency interference (RFI) become more apparent. Near radio telescope arrays,
RFI sources are often easily removed or replaced; the challenge lies in
identifying them. Transient (impulsive) RFI is particularly difficult to
identify. We propose a novel dictionary-based approach to transient RFI
identification. RFI events are treated as sequences of sub-events, drawn from
particular labelled classes. We demonstrate an automated method of extracting
and labelling sub-events using a dataset of transient RFI. A dictionary of
labels may be used in conjunction with hidden Markov models to identify the
sources of RFI events reliably. We attain improved classification accuracy over
traditional approaches such as SVMs or a naïve kNN classifier. Finally, we
investigate why transient RFI is difficult to classify. We show that cluster
separation in the principal components domain is influenced by the mains supply
phase for certain sources.