scholarly journals Comparison of two cyclostationary detectors for radio frequency interference mitigation in radio astronomy

Radio Science ◽  
2005 ◽  
Vol 40 (5) ◽  
pp. n/a-n/a ◽  
Author(s):  
S. Bretteil ◽  
R. Weber
Keyword(s):  
Radio Frequency ◽  
Radio Astronomy ◽  
Interference Mitigation ◽  
Radio Frequency Interference

scholarly journals Radio Frequency Interference Mitigation and Statistics in the Spectral Observations of FAST

2021 ◽  
Author(s):  
Chuan-Peng Zhang ◽  
Jin-Long Xu ◽  
Jie Wang ◽  
Yingjie Jing ◽  
Ziming Liu ◽  
...  
Keyword(s):  
Data Processing ◽  
Radio Frequency ◽  
Radio Telescope ◽  
Radio Astronomy ◽  
Interference Mitigation ◽  
Statistical Data ◽  
Radio Frequency Interference ◽  
Test Results ◽  
Baseline Fitting ◽  
Statistical Results

Abstract In radio astronomy, radio frequency interference (RFI) becomes more and more serious for radio observational facilities. The RFI always influences the search and study of the interesting astronomical objects. Mitigating the RFI becomes an essential procedure in any survey data processing. Five-hundred-meter Aperture Spherical radio Telescope (FAST) is an extremely sensitive radio telescope. It is necessary to find out an effective and precise RFI mitigation method for FAST data processing. In this work, we introduce a method to mitigate the RFI in FAST spectral observation and make a statistics for the RFI using ∼300 hours FAST data. The details are as follows. Firstly, according to the characteristics of FAST spectra, we propose to use the ArPLS algorithm for baseline fitting. Our test results show that it has a good performance. Secondly, we flag the RFI with four strategies, which are to flag extremely strong RFI, flag long-lasting RFI, flag polarized RFI, and flag beam-combined RFI, respectively. The test results show that all the RFI above a preset threshold could be flagged. Thirdly, we make a statistics for the probabilities of polarized XX and YY RFI in FAST observations. The statistical results could tell us which frequencies are relatively quiescent. With such statistical data, we are able to avoid using such frequencies in our spectral observations. Finally, based on the ∼300 hours FAST data, we got an RFI table, which is the most complete database currently for FAST.

Low-frequency technology for a lunar interferometer

2020 ◽  
Vol 379 (2188) ◽  
pp. 20190575 ◽  
Author(s):  
Kristian Zarb Adami ◽  
I. O. Farhat
Keyword(s):  
Radio Frequency ◽  
Signal Detection ◽  
Radio Astronomy ◽  
Lunar Surface ◽  
Low Frequency ◽  
Radio Interferometer ◽  
Radio Frequency Interference ◽  
The Moon ◽  
Single Antenna ◽  
Global Signal

This work sketches a possible design architecture of a low-frequency radio interferometer located on the lunar surface. The design has evolved from single antenna experiments aimed at the global signal detection of the epoch of reionization (EoR) to the square kilometre array (SKA) which, when complete, will be capable of imaging the highly red-shifted H 1 -signal from the cosmic dawn through to the EoR. However, due to the opacity of the ionosphere below 10 MHz and the anthropogenic radio-frequency interference, these terrestrial facilities are incapable of detecting pre-ionization signals and the moon becomes an attractive location to build a low-frequency radio interferometer capable of detecting such cosmological signals. Even though there are enormous engineering challenges to overcome, having this scientific facility on the lunar surface also opens up several new exciting possibilities for low-frequency radio astronomy. This article is part of a discussion meeting issue ‘Astronomy from the Moon: the next decades’.

scholarly journals Deep learning improves identification of Radio Frequency Interference

2020 ◽  
Vol 499 (1) ◽  
pp. 379-390
Author(s):  
Alireza Vafaei Sadr ◽  
Bruce A Bassett ◽  
Nadeem Oozeer ◽  
Yabebal Fantaye ◽  
Chris Finlay
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Radio Astronomy ◽  
Real Data ◽  
Radio Frequency Interference ◽  
Time Frequency ◽  
Amplitude Data ◽  
Important Challenge ◽  
Telescope Arrays

ABSTRACT Flagging of Radio Frequency Interference (RFI) in time–frequency visibility data is an increasingly important challenge in radio astronomy. We present R-Net, a deep convolutional ResNet architecture that significantly outperforms existing algorithms – including the default MeerKAT RFI flagger, and deep U-Net architectures – across all metrics including AUC, F1-score, and MCC. We demonstrate the robustness of this improvement on both single dish and interferometric simulations and, using transfer learning, on real data. Our R-Net model’s precision is approximately $90{{\ \rm per\ cent}}$ better than the current MeerKAT flagger at $80{{\ \rm per\ cent}}$ recall and has a 35 per cent higher F1-score with no additional performance cost. We further highlight the effectiveness of transfer learning from a model initially trained on simulated MeerKAT data and fine-tuned on real, human-flagged, KAT-7 data. Despite the wide differences in the nature of the two telescope arrays, the model achieves an AUC of 0.91, while the best model without transfer learning only reaches an AUC of 0.67. We consider the use of phase information in our models but find that without calibration the phase adds almost no extra information relative to amplitude data only. Our results strongly suggest that deep learning on simulations, boosted by transfer learning on real data, will likely play a key role in the future of RFI flagging of radio astronomy data.

scholarly journals Adaptive Radio Frequency Interference Mitigation for Passive Bistatic Radar Using OFDM Waveform

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Zhixin Zhao ◽  
Sihang Zhu ◽  
Yuhao Wang ◽  
Siyuan Cheng ◽  
Sheng Hong
Keyword(s):  
Radio Frequency ◽  
Orthogonal Frequency Division Multiplexing ◽  
Interference Mitigation ◽  
Short Wave ◽  
Good Choice ◽  
Hf Radar ◽  
Bistatic Radar ◽  
Radio Frequency Interference ◽  
Wave Band ◽  
Passive Bistatic Radar

High frequency passive bistatic radar (HFPBR) is a novel and promising technique in development. DRM broadcast exploiting orthogonal frequency division multiplexing (OFDM) technique supplies a good choice for the illuminator of HFPBR. HFPBR works in crowded short wave band. It faces severe radio frequency interference (RFI) problem. In this paper, a theoretical analysis of the range-domain correlation of RFI in OFDM-based HF radar is presented. A RFI mitigation method in the range domain is introduced. After the direct-path wave rejection, the interference subspace is constructed using the echo signals at the reserved range bins. Then RFI in the effective range bins is mitigated by the subspace projection, using the correlation among different range bins. The introduced algorithm is easy to perform in practice and the RFI mitigation performance is evaluated using the experimental data of DRM-based HFPBR.

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