TY - GEN
T1 - Efficient rfi detection in radio astronomy based on compressive statistical sensing
AU - Cucho-Padin, Gonzalo
AU - Wang, Yue
AU - Waldrop, Lara
AU - Tian, Zhi
AU - Kamalabadi, Farzad
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data. We demonstrate the performance of this algorithm by analyzing radio astronomy data acquired from the Arecibo Observatory (AO)'s L-Wide band receiver (~1.3 GHz), which is typically corrupted by active radars for commercial applications located near AO facilities. Our CSS-based solution enables robust and efficient detection of the RFI frequency bands present in the data, which is measured by receiver operating characteristic (ROC) curves. As a result, it allows fast and computationally efficient identification of RFI-free frequency regions in wideband radio astronomy observations.
AB - In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data. We demonstrate the performance of this algorithm by analyzing radio astronomy data acquired from the Arecibo Observatory (AO)'s L-Wide band receiver (~1.3 GHz), which is typically corrupted by active radars for commercial applications located near AO facilities. Our CSS-based solution enables robust and efficient detection of the RFI frequency bands present in the data, which is measured by receiver operating characteristic (ROC) curves. As a result, it allows fast and computationally efficient identification of RFI-free frequency regions in wideband radio astronomy observations.
KW - Compressive statistical sensing
KW - Cyclic spectrum
KW - Radio astronomy
KW - Radio frequency interference
UR - http://www.scopus.com/inward/record.url?scp=85063089322&partnerID=8YFLogxK
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U2 - 10.1109/GlobalSIP.2018.8646517
DO - 10.1109/GlobalSIP.2018.8646517
M3 - Conference contribution
AN - SCOPUS:85063089322
T3 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
SP - 1109
EP - 1113
BT - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Y2 - 26 November 2018 through 29 November 2018
ER -