TY - JOUR
T1 - Statistical hypothesis testing and variance analysis for radio frequency interference identification in solar data
AU - Wang, Xiaoli
AU - Ge, Hongya
AU - Gary, Dale
AU - Nita, Gelu
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2009/10
Y1 - 2009/10
N2 - 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.
AB - 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.
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U2 - 10.1086/644792
DO - 10.1086/644792
M3 - Article
AN - SCOPUS:70349871295
VL - 121
SP - 1139
EP - 1150
JO - Publications of the Astronomical Society of the Pacific
JF - Publications of the Astronomical Society of the Pacific
SN - 0004-6280
IS - 884
ER -