Statistical hypothesis testing and variance analysis for radio frequency interference identification in solar data

Document Type

Article

Publication Date

10-1-2009

Abstract

This work presents an effective algorithm for radio frequency interference (RFI) identification using dynamic power spectrum statistics in the frequency domain. Statistical signal processing techniques such as hypothesis testing and variance analysis are utilized to derive a test statistic for effective and efficient RFI identification. Starting from the generalized likelihood ratio test (GLRT), we formulate the problem systematically and propose a practical test statistic T(x; f), shown to be F distributed, for RFI identification. A threshold approach working on this test statistic is developed to identify the presence of narrowband RFI in the power spectrum with additive Gaussian noise and/or solar flare background, corresponding to a desired constant false alarm rate (CFAR). Detailed analysis on detector performance and effect of RFI duty cycle are also provided. The proposed statistical test is applied to experimental solar data collected by our frequency-agile solar radio telescope (FASR) subsystem testbed (FST) to demonstrate the robustness and scalability of the algorithm, as well as its capability for real-time implementation. © 2009. The Astronomical Society of the Pacific.

Identifier

70349871295 (Scopus)

Publication Title

Publications of the Astronomical Society of the Pacific

External Full Text Location

https://doi.org/10.1086/644792

ISSN

00046280

First Page

1139

Last Page

1150

Issue

884

Volume

121

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