Time domain classification of transient radio frequency interference

Daniel Czech, Amit Kumar Mishra, Michael Inggs

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

Abstract

Short, transient radio-frequency interference (RFI) events could threaten the quality of astronomical observations made by new and planned radio telescopes such as MeerKAT, the SKA and HERA in the radio quiet reserve in South Africa. Because they are so short, often of the order of microseconds long, these events are difficult to detect and identify in the time-frequency plots typically produced by RFI monitoring systems. In this paper, we record and analyse a dataset of the time domain RFI signals of nine typical transient RFI sources. We show that it is possible to classify such transient signals in the time domain according to their source using Principal Components Analysis (PCA) and Kernel PCA. Using an adapted measure of cluster separation, we show that Kernel PCA is significantly better than standard PCA at distinguishing transient RFI sources from one another.

Original languageEnglish
Title of host publication2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PublisherIEEE Press
Pages302-305
Number of pages4
ISBN (Electronic)9781509033324
DOIs
Publication statusPublished - 10 Jul 2016
Externally publishedYes
Event36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China
Duration: 10 Jul 201615 Jul 2016

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2016-November

Conference

Conference36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/TerritoryChina
CityBeijing
Period10 Jul 201615 Jul 2016

Keywords

  • kernel principal components analysis
  • principal components analysis
  • Radio frequency interference
  • time domain classification

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