A Dictionary Approach to Identifying Transient RFI

As radio telescopes become more sensitive, the damaging effects of radio frequency interference (RFI) become more apparent. One particular approach to mitigating RFI is to deploy independent, omnidirectional wideband monitoring systems at radio telescope arrays. Near such radio telescopes, detected 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 subevents, drawn from particular labeled classes. We demonstrate an automated method of extracting and labeling subevents using a data set 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 support vector machines or a naïve k-Nearest Neighbor 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 alternating current supply phase for certain sources.