scholarly journals Stochastic calibration of radio interferometers

2020 ◽  
Vol 493 (4) ◽  
pp. 6071-6078 ◽  
Author(s):  
Sarod Yatawatta
Keyword(s):  
Data Processing ◽  
Radio Frequency ◽  
Computational Cost ◽  
Spatial Localization ◽  
Radio Frequency Interference ◽  
Radio Telescopes ◽  
Data Rates ◽  
Data Volume ◽  
Radio Transients ◽  
Processing Steps

ABSTRACT With ever-increasing data rates produced by modern radio telescopes like LOFAR and future telescopes like the SKA, many data-processing steps are overwhelmed by the amount of data that needs to be handled using limited compute resources. Calibration is one such operation that dominates the overall data processing computational cost; none the less, it is an essential operation to reach many science goals. Calibration algorithms do exist that scale well with the number of stations of an array and the number of directions being calibrated. However, the remaining bottleneck is the raw data volume, which scales with the number of baselines, and which is proportional to the square of the number of stations. We propose a ‘stochastic’ calibration strategy where we read only in a mini-batch of data for obtaining calibration solutions, as opposed to reading the full batch of data being calibrated. None the less, we obtain solutions that are valid for the full batch of data. Normally, data need to be averaged before calibration is performed to accommodate the data in size-limited compute memory. Stochastic calibration overcomes the need for data averaging before any calibration can be performed, and offers many advantages, including: enabling the mitigation of faint radio frequency interference; better removal of strong celestial sources from the data; and better detection and spatial localization of fast radio transients.

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.

scholarly journals Radio Frequency Interference Site Survey for Thai Radio Telescopes

2017 ◽  
Vol 901 ◽  
pp. 012062
Author(s):  
P Jaroenjittichai ◽  
S Punyawarin ◽  
D Singwong ◽  
P Somboonpon ◽  
N Prasert ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Interference ◽  
Radio Telescopes ◽  
Site Survey

Radio frequency interference mitigation based on the asymmetrically reweighted penalized least squares and SumThreshold method

2020 ◽  
Vol 500 (3) ◽  
pp. 2969-2978
Author(s):  
Qingguo Zeng ◽  
Xue Chen ◽  
Xiangru Li ◽  
J L Han ◽  
Chen Wang ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Least Squares ◽  
Interference Mitigation ◽  
Detection Algorithm ◽  
Radio Frequency Interference ◽  
Penalized Least Squares ◽  
Radio Telescopes ◽  
Processing Pipeline ◽  
Different Types ◽  
And Performance

ABSTRACT As radio telescopes become more sensitive, radio frequency interference (RFI) is becoming more important for interesting signals of radio astronomy. There is a demand for developing an automatic, accurate and efficient RFI mitigation method. Therefore, we have investigated an RFI detection algorithm. First, we introduce an asymmetrically reweighted penalized least squares (ArPLS) method to estimate the baseline more accurately. After removing the estimated baseline, several novel strategies were proposed based on the SumThreshold algorithm for detecting different types of RFI. The threshold parameter in SumThreshold can be determined automatically and adaptively. The adaptiveness is essential for reducing human intervention and for the online RFI processing pipeline. Applications to data from the Five-hundred-meter Aperture Spherical Telescope (FAST) show that the proposed scheme based on ArPLS and SumThreshold is superior to some typically available methods for RFI detection with respect to efficiency and performance.

scholarly journals Protecting Space-Based Radio Astronomy

2001 ◽  
Vol 196 ◽  
pp. 324-334 ◽  
Author(s):  
V. Altunin
Keyword(s):  
Radio Frequency ◽  
Radio Astronomy ◽  
Low Frequency ◽  
Radio Frequency Interference ◽  
Lagrangian Point ◽  
Radio Telescopes ◽  
The Moon ◽  
Microwave Background ◽  
Distant Future ◽  
First Results

This paper outlines some of the radio frequency interference issues related to radio astronomy performed with space-based radio telescopes. Radio frequency interference that threatens radio astronomy observations from the surface of Earth will also degrade observations with space-based radio telescopes. However, any resulting interference could be different than for ground-based telescopes due to several factors. Space radio astronomy observations significantly enhance studies in different areas of astronomy. Several space radio astronomy experiments for studies in low-frequency radio astronomy, space VLBI, the cosmic microwave background and the submillimetre wavelengths have flown already. The first results from these missions have provided significant breakthroughs in our understanding of the nature of celestial radio radiation. Radio astronomers plan to deploy more radio telescopes in Earth orbit, in the vicinity of the L2 Sun-Earth Lagrangian point, and, in the more distant future, in the shielded zone of the Moon.

scholarly journals RFI Sentinel 2

2001 ◽  
Vol 196 ◽  
pp. 301-306
Author(s):  
S. Montebugnoli ◽  
M. Cecchi ◽  
C. Bortolotti ◽  
M. Roma ◽  
S. Mariotti
Keyword(s):  
Radio Frequency ◽  
Monitoring System ◽  
Radio Astronomy ◽  
State Of The Art ◽  
Radio Frequency Interference ◽  
Radio Telescopes ◽  
New Approach ◽  
Frequency Bands ◽  
Application Fields ◽  
Sentinel 2

Nowadays we have a massively increasing use of radio techniques in a wide variety of application fields. Meanwhile state-of-the-art receiver technology dramatically increases the sensitivity of modern radio telescopes. This situation produces a worrying vulnerability of ground-based radio telescopes to Radio Frequency Interference (RFI). In order to monitor the RFI scenario within the frequency bands reserved for radio astronomy activities, a monitoring system, based on a quite new approach, has been developed and is presented here.

ANALYSIS OF POTENTIAL RADIO FREQUENCY INTERFERENCE ON ANTENNA POSITIONER (3M) IN RANCABUNGUR GROUND STATION

2019 ◽  
Author(s):  
Rumadi Rumadi ◽  
◽  
Dicka Ariptian Rahayu ◽  
Nur Salma Yusuf Hasanah ◽  
Zhauhar Rainaldy Ardhana ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Interference ◽  
Ground Station

Software platform for automated preparation and analytical processing of heterogeneous datasets

2020 ◽  
pp. 15-20 ◽  
Author(s):  
E. D. Avedyan ◽  
I. V. Voronkov
Keyword(s):  
Data Processing ◽  
Analytical Data ◽  
Volume Growth ◽  
Software Platform ◽  
External Data ◽  
Heterogeneous Datasets ◽  
Data Volume ◽  
Processing Resources ◽  
Modular Platform ◽  
Network Technologies

Summary: the article proposes new software platform for automating the processes of preprocessing and marking up datasets with the aim of further solving analytical problems such as image classification and processing textual and parametric information using neural network technologies. The software platform uses modern technologies and combines a large number of methods in the form of a modular platform, which can be supplemented as the tasks of analytical data processing become more complicated. The need to develop such a software platform is dictated primarily by the fact that, given the current level of data volume growth, the actual transition to deep data analytics remains unattainable without such software platforms, since confidentiality, access to information and the use of external data processing resources are required.

scholarly journals An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6885
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Video Stream ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
Continuous Wave Interference

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

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