Implementing RFI Mitigation in Radio Science

2019 ◽  
Vol 08 (01) ◽  
pp. 1940010 ◽  
Author(s):  
Willem A. Baan
Keyword(s):  
Radio Frequency ◽  
Data Acquisition ◽  
Radio Astronomy ◽  
Data Acquisition System ◽  
Detection Methods ◽  
Radio Frequency Interference ◽  
Post Processing ◽  
Science Data ◽  
Radio Science ◽  
Mitigation Methods

This paper presents an overview of methods for mitigating radio frequency interference (RFI) in radio science data. The primary purpose of mitigation is to assist observatories to take useful data outside frequency bands allocated to the Science Services (Radio Astronomy Service (RAS) and Earth Exploration Service (EESS)): mitigation should not be needed within passive bands. Mitigation methods may be introduced at a variety of points within the data acquisition system in order to lessen the RFI intensity and to limit the damage it causes. These methods range from proactive methods to changing the local RFI environment by means of regulatory manners, to pre- and post-detection methods, to various pre-processing methods, and to methods applied at or post-processing.

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 A Database for the Radio Frequency Fingerprinting of Bluetooth Devices

Data ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 55
Author(s):  
Emre Uzundurukan ◽  
Yaser Dalveren ◽  
Ali Kara
Keyword(s):  
Wireless Networks ◽  
Radio Frequency ◽  
Data Acquisition ◽  
Data Acquisition System ◽  
Physical Layer ◽  
Acquisition System ◽  
Wireless Devices ◽  
Malicious Attacks ◽  
Distinctive Features ◽  
Important Requirement

Radio frequency fingerprinting (RFF) is a promising physical layer protection technique which can be used to defend wireless networks from malicious attacks. It is based on the use of the distinctive features of the physical waveforms (signals) transmitted from wireless devices in order to classify authorized users. The most important requirement to develop an RFF method is the existence of a precise, robust, and extensive database of the emitted signals. In this context, this paper introduces a database consisting of Bluetooth (BT) signals collected at different sampling rates from 27 different smartphones (six manufacturers with several models for each). Firstly, the data acquisition system to create the database is described in detail. Then, the two well-known methods based on transient BT signals are experimentally tested by using the provided data to check their solidity. The results show that the created database may be useful for many researchers working on the development of the RFF of BT devices.

scholarly journals A survey of spatially and temporally resolved radio frequency interference in the FM band at the Murchison Radio-astronomy Observatory

2020 ◽  
Vol 37 ◽  
Author(s):  
S. J. Tingay ◽  
M. Sokolowski ◽  
R. Wayth ◽  
D. Ung
Keyword(s):  
Radio Frequency ◽  
Radio Astronomy ◽  
Angular Resolution ◽  
Low Frequency ◽  
Earth Orbit ◽  
Radio Frequency Interference ◽  
Astronomy Observatory ◽  
Frequency Range ◽  
Radio Astronomy Observatory ◽  
Imaging Mode

Abstract We present the first survey of radio frequency interference (RFI) at the future site of the low frequency Square Kilometre Array (SKA), the Murchison Radio-astronomy Observatory (MRO), that both temporally and spatially resolves the RFI. The survey is conducted in a 1 MHz frequency range within the FM band, designed to encompass the closest and strongest FM transmitters to the MRO (located in Geraldton, approximately 300 km distant). Conducted over approximately three days using the second iteration of the Engineering Development Array in an all-sky imaging mode, we find a range of RFI signals. We are able to categorise the signals into: those received directly from the transmitters, from their horizon locations; reflections from aircraft (occupying approximately 13% of the observation duration); reflections from objects in Earth orbit; and reflections from meteor ionisation trails. In total, we analyse 33 994 images at 7.92 s time resolution in both polarisations with angular resolution of approximately 3.5 $^{\circ}$ , detecting approximately forty thousand RFI events. This detailed breakdown of RFI in the MRO environment will enable future detailed analyses of the likely impacts of RFI on key science at low radio frequencies with the SKA.

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.

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