Pub Date : 2019-09-01DOI: 10.23919/RFI48793.2019.9111713
R. Wolhuter, Jason Fynn, C. van der Merwe, A. Otto, Johannes P. Havenga
The abstract should appear at the top of the left-hand column of text, about 0.5 inch (12 mm) below the title area and no more than 3.125 inches (80 mm) in length. Leave a 0.5 inch (12 mm) space between the end of the abstract and the beginning of the main text. The abstract should contain about 100 to 150 words, and should be identical to the abstract text submitted electronically along with the paper cover sheet. All manuscripts must be in English, printed in black ink.
{"title":"A Band Comparison Investigation for RFI Emission Mitigation by a Mobile Radio Communications Network for the SKA Radio Astronomy Project","authors":"R. Wolhuter, Jason Fynn, C. van der Merwe, A. Otto, Johannes P. Havenga","doi":"10.23919/RFI48793.2019.9111713","DOIUrl":"https://doi.org/10.23919/RFI48793.2019.9111713","url":null,"abstract":"The abstract should appear at the top of the left-hand column of text, about 0.5 inch (12 mm) below the title area and no more than 3.125 inches (80 mm) in length. Leave a 0.5 inch (12 mm) space between the end of the abstract and the beginning of the main text. The abstract should contain about 100 to 150 words, and should be identical to the abstract text submitted electronically along with the paper cover sheet. All manuscripts must be in English, printed in black ink.","PeriodicalId":111866,"journal":{"name":"2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115803339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.23919/RFI48793.2019.9111748
Kyle Harrison, A. Mishra
Neural network (NN) based methods are applied to the detection of radio frequency interference (RFI) in post-correlation, post-calibration time/frequency data. While calibration does affect RFI for the sake of this work a reduced dataset in post-calibration is used. Two machine learning approaches for flagging real measurement data are demonstrated using the existing RFI flagging technique AOFlagger as a ground truth. It is shown that a single layer fully connect network can be trained using each time/frequency sample individually with the magnitude and phase of each polarization and Stokes visibilities as features. This method was able to predict a Boolean flag map for each baseline to a high degree of accuracy achieving a Recall of 0.69 and Precision of 0.83 and an F1-Score of 0.75.The second approach utilizes a convolutional neural network (CNN) implemented in the U-Net architecture, shown in literature to work effectively on simulated radio data. In this work the architecture trained on real data results in a Recall, Precision and F1-Score 0.84, 0.91, 0.87 respectfully.This work seeks to investigate the application of supervised learning when trained on a ground truth from existing flagging techniques, the results of which inherently contain false positives. In order for a fair comparison to be made the data is imaged using CASA’s CLEAN algorithm and the UNet and NN’s flagging results allow for 5 and 6 additional radio sources to be identified respectively.
{"title":"Supervised Neural Networks for RFI Flagging","authors":"Kyle Harrison, A. Mishra","doi":"10.23919/RFI48793.2019.9111748","DOIUrl":"https://doi.org/10.23919/RFI48793.2019.9111748","url":null,"abstract":"Neural network (NN) based methods are applied to the detection of radio frequency interference (RFI) in post-correlation, post-calibration time/frequency data. While calibration does affect RFI for the sake of this work a reduced dataset in post-calibration is used. Two machine learning approaches for flagging real measurement data are demonstrated using the existing RFI flagging technique AOFlagger as a ground truth. It is shown that a single layer fully connect network can be trained using each time/frequency sample individually with the magnitude and phase of each polarization and Stokes visibilities as features. This method was able to predict a Boolean flag map for each baseline to a high degree of accuracy achieving a Recall of 0.69 and Precision of 0.83 and an F1-Score of 0.75.The second approach utilizes a convolutional neural network (CNN) implemented in the U-Net architecture, shown in literature to work effectively on simulated radio data. In this work the architecture trained on real data results in a Recall, Precision and F1-Score 0.84, 0.91, 0.87 respectfully.This work seeks to investigate the application of supervised learning when trained on a ground truth from existing flagging techniques, the results of which inherently contain false positives. In order for a fair comparison to be made the data is imaged using CASA’s CLEAN algorithm and the UNet and NN’s flagging results allow for 5 and 6 additional radio sources to be identified respectively.","PeriodicalId":111866,"journal":{"name":"2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126010677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.23919/RFI48793.2019.9111666
Stephen T. Harrison, Rory Coles, T. Robishaw, D. D. Del Rizzo
In order to ensure that the Dominion Radio Astrophysical Observatory (DRAO) continues to be a great asset to the Canadian astronomical community we must work to actively protect the RF cleanliness of the site. One aspect of this much larger effort is the site monitor project. This is currently realized by an omnidirectional monitoring station mounted on the roof of the main building.A pitfall of previous RFI monitoring projects on site has been the volume of data produced, combined with the time limitations of personnel. Occupancy plots have been produced, but this tool has very limited value for day-to-day maintenance of the site. Simply, no eyes have been available to look at all of the data.Our aim is to deal with the data first: to build a rich description of the RF scene at the site in order to automatically separate “normal” events from “novel” events. To do this we use features extracted from both the spectrogram and the complex baseband waveform. This includes center frequency, bandwidth, received power, transmission duration, time of day, high-order cumulants, and more. We use unsupervised learning techniques to cluster events in this multidimensional space into hierarchical groups. The clustering results allow us to study populations of events and their relationships, rather than individual or small sets of events as in a spectrogram. This feature space also allows us to relate waveforms with similar modulations across frequency, and to reveal temporal patterns. Work is ongoing to bring this analysis into a realtime observing state, in order to provide up-to-date notifications about novel RF events occurring at the DRAO site.
{"title":"RFI Novelty Detection using Machine Learning Techniques","authors":"Stephen T. Harrison, Rory Coles, T. Robishaw, D. D. Del Rizzo","doi":"10.23919/RFI48793.2019.9111666","DOIUrl":"https://doi.org/10.23919/RFI48793.2019.9111666","url":null,"abstract":"In order to ensure that the Dominion Radio Astrophysical Observatory (DRAO) continues to be a great asset to the Canadian astronomical community we must work to actively protect the RF cleanliness of the site. One aspect of this much larger effort is the site monitor project. This is currently realized by an omnidirectional monitoring station mounted on the roof of the main building.A pitfall of previous RFI monitoring projects on site has been the volume of data produced, combined with the time limitations of personnel. Occupancy plots have been produced, but this tool has very limited value for day-to-day maintenance of the site. Simply, no eyes have been available to look at all of the data.Our aim is to deal with the data first: to build a rich description of the RF scene at the site in order to automatically separate “normal” events from “novel” events. To do this we use features extracted from both the spectrogram and the complex baseband waveform. This includes center frequency, bandwidth, received power, transmission duration, time of day, high-order cumulants, and more. We use unsupervised learning techniques to cluster events in this multidimensional space into hierarchical groups. The clustering results allow us to study populations of events and their relationships, rather than individual or small sets of events as in a spectrogram. This feature space also allows us to relate waveforms with similar modulations across frequency, and to reveal temporal patterns. Work is ongoing to bring this analysis into a realtime observing state, in order to provide up-to-date notifications about novel RF events occurring at the DRAO site.","PeriodicalId":111866,"journal":{"name":"2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126709895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.23919/rfi48793.2019.9111795
{"title":"RFI 2019 Summary of the RFI 2019 Workshop","authors":"","doi":"10.23919/rfi48793.2019.9111795","DOIUrl":"https://doi.org/10.23919/rfi48793.2019.9111795","url":null,"abstract":"","PeriodicalId":111866,"journal":{"name":"2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122211003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.23919/RFI48793.2019.9111839
T. Robishaw, Stephen T. Harrison, D. D. Del Rizzo, R. Messing, Benoit Robert
In 1957 the site for the Dominion Radio Astrophysical Observatory was chosen for its minimal radio frequency interference (RFI) from nearby human activities. The efficacy of the site has since been compromised by having a full staff of 40+ engineers, technologists, and scientists located directly adjacent to the telescopes. The site is visited daily by temporary workers, the general public, couriers, and contractors. Guided tours are offered 10AM–5PM every weekend from April through October, significantly increasing the number of visitors. The encroachment of humans on site brings RFI that is carried on their persons, such as cell phones, tablets, smart watches, and cameras. We built a device to detect cellular uplink activity as a means to demonstrating an affordable off-the-shelf solution for finding bad actors on site who have not powered off their cellular devices. We describe the cell-phone buster device, show the results from a brief survey of on-site cellular activity, and discuss plans to expand the capabilities to detect Wi-Fi, Bluetooth, and other RF emissions from personal electronic devices on site.
{"title":"Bustin’ Makes Me Feel Good: A Low-Cost Cell-Phone Buster for the 850 MHz Band","authors":"T. Robishaw, Stephen T. Harrison, D. D. Del Rizzo, R. Messing, Benoit Robert","doi":"10.23919/RFI48793.2019.9111839","DOIUrl":"https://doi.org/10.23919/RFI48793.2019.9111839","url":null,"abstract":"In 1957 the site for the Dominion Radio Astrophysical Observatory was chosen for its minimal radio frequency interference (RFI) from nearby human activities. The efficacy of the site has since been compromised by having a full staff of 40+ engineers, technologists, and scientists located directly adjacent to the telescopes. The site is visited daily by temporary workers, the general public, couriers, and contractors. Guided tours are offered 10AM–5PM every weekend from April through October, significantly increasing the number of visitors. The encroachment of humans on site brings RFI that is carried on their persons, such as cell phones, tablets, smart watches, and cameras. We built a device to detect cellular uplink activity as a means to demonstrating an affordable off-the-shelf solution for finding bad actors on site who have not powered off their cellular devices. We describe the cell-phone buster device, show the results from a brief survey of on-site cellular activity, and discuss plans to expand the capabilities to detect Wi-Fi, Bluetooth, and other RF emissions from personal electronic devices on site.","PeriodicalId":111866,"journal":{"name":"2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121121171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.23919/RFI48793.2019.9111826
A. Sclocco, D. Vohl, R. V. Nieuwpoort
Current and upcoming radio telescopes are being designed with increasing sensitivity to detect new and mysterious radio sources of astrophysical origin. While this increased sensitivity improves the likelihood of discoveries, it also makes these instruments more susceptible to the deleterious effects of Radio Frequency Interference (RFI). The challenge posed by RFI is exacerbated by the high data-rates achieved by modern radio telescopes, which require real-time processing to keep up with the data. Furthermore, the high data-rates do not allow for permanent storage of observations at high resolution. Offline RFI mitigation is therefore not possible anymore. The real-time requirement makes RFI mitigation even more challenging because, on one side, the techniques used for mitigation need to be fast and simple, and on the other side they also need to be robust enough to cope with just a partial view of the data.The Apertif Radio Transient System (ARTS) is the real-time, time-domain, transient detection instrument of the Westerbork Synthesis Radio Telescope (WSRT), and it is a perfect example of this challenging scenario. This system processes 73 Gb of data per second, in real-time, searching for faint pulsars and Fast Radio Bursts. Despite the radio quiet zone around WSRT, the generation of RFI is becoming increasingly part of anthropic activities, especially in a densely populated environment like the Netherlands where the telescope is located. Furthermore, our sky is populated by a growing number of satellites for world-wide telecommunication. Hence, the ARTS pipeline requires state-of-the-art real-time RFI mitigation, even if it contains a deep learning classifier to reduce the number of false-positive detections.Our solution to this challenge is RFIm, a high-performance, open-source, tuned, and extensible RFI mitigation library. The goal of this library is to provide users with RFI mitigation routines that are designed to run in real-time on many-core accelerators, such as Graphics Processing Units, and that can be highly-tuned to achieve code and performance portability to different hardware platforms and scientific use-cases. Results on ARTS show that we can achieve real-time RFI mitigation, with a minimal impact on the total execution time of the search pipeline, and considerably reduce the number of false-positives.
{"title":"Real-Time RFI Mitigation for the Apertif Radio Transient System","authors":"A. Sclocco, D. Vohl, R. V. Nieuwpoort","doi":"10.23919/RFI48793.2019.9111826","DOIUrl":"https://doi.org/10.23919/RFI48793.2019.9111826","url":null,"abstract":"Current and upcoming radio telescopes are being designed with increasing sensitivity to detect new and mysterious radio sources of astrophysical origin. While this increased sensitivity improves the likelihood of discoveries, it also makes these instruments more susceptible to the deleterious effects of Radio Frequency Interference (RFI). The challenge posed by RFI is exacerbated by the high data-rates achieved by modern radio telescopes, which require real-time processing to keep up with the data. Furthermore, the high data-rates do not allow for permanent storage of observations at high resolution. Offline RFI mitigation is therefore not possible anymore. The real-time requirement makes RFI mitigation even more challenging because, on one side, the techniques used for mitigation need to be fast and simple, and on the other side they also need to be robust enough to cope with just a partial view of the data.The Apertif Radio Transient System (ARTS) is the real-time, time-domain, transient detection instrument of the Westerbork Synthesis Radio Telescope (WSRT), and it is a perfect example of this challenging scenario. This system processes 73 Gb of data per second, in real-time, searching for faint pulsars and Fast Radio Bursts. Despite the radio quiet zone around WSRT, the generation of RFI is becoming increasingly part of anthropic activities, especially in a densely populated environment like the Netherlands where the telescope is located. Furthermore, our sky is populated by a growing number of satellites for world-wide telecommunication. Hence, the ARTS pipeline requires state-of-the-art real-time RFI mitigation, even if it contains a deep learning classifier to reduce the number of false-positive detections.Our solution to this challenge is RFIm, a high-performance, open-source, tuned, and extensible RFI mitigation library. The goal of this library is to provide users with RFI mitigation routines that are designed to run in real-time on many-core accelerators, such as Graphics Processing Units, and that can be highly-tuned to achieve code and performance portability to different hardware platforms and scientific use-cases. Results on ARTS show that we can achieve real-time RFI mitigation, with a minimal impact on the total execution time of the search pipeline, and considerably reduce the number of false-positives.","PeriodicalId":111866,"journal":{"name":"2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126574925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.23919/RFI48793.2019.9111769
Alexis Louis
Distortions of GNSS signals can lead to unacceptable pseudo-range errors. The object of study is a certain type of distortion — evil waveforms (EWF) — which is a rare perturbation occuring at the stage of signal generation. Detecting those distortions post-correlation traditionally involve designing hand crafted structure tests on a densely sampled autocorrelation function (ACF). However, traditional hand crafted tests have to be designed for specific scenarios hence lack flexibility compared to data-based methods. A neural network architecture capable of processing the structure of the ACF is proposed, implicitly learning structure tests, in order to tackle the evil waveforms detection problem.
{"title":"Neural Network Based Evil Waveforms Detection","authors":"Alexis Louis","doi":"10.23919/RFI48793.2019.9111769","DOIUrl":"https://doi.org/10.23919/RFI48793.2019.9111769","url":null,"abstract":"Distortions of GNSS signals can lead to unacceptable pseudo-range errors. The object of study is a certain type of distortion — evil waveforms (EWF) — which is a rare perturbation occuring at the stage of signal generation. Detecting those distortions post-correlation traditionally involve designing hand crafted structure tests on a densely sampled autocorrelation function (ACF). However, traditional hand crafted tests have to be designed for specific scenarios hence lack flexibility compared to data-based methods. A neural network architecture capable of processing the structure of the ACF is proposed, implicitly learning structure tests, in order to tackle the evil waveforms detection problem.","PeriodicalId":111866,"journal":{"name":"2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124171598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.23919/RFI48793.2019.9111822
G. Hovey, Federico Di Vruno
Modern radio telescopes, like the proposed Square Kilometre Array (SKA), are extremely sensitive and the faint signals they receive can easily be contaminated irreversibly by stray radio frequency interference (RFI). Understanding how radio telescope performance is degraded by RFI is important. In this paper we describe an RFI simulation framework that can be used to generate test stimulus and verify a telescope’s performance. The framework can be used during design to investigate the impact of various RFI scenarios and develop mitigation strategies. As well, it can be used to exercise and test hardware firmware after a system is installed. A prototype of the framework was implemented in the Python computer language to demonstrate the key concepts. Additionally, we outline the framework requirements, describe a suitable software structure and discuss a prototype implementation. As well, we present measurements made to verify the software generates correct test stimulus, for RFI from aircraft distance measuring equipment (DME). The work described was carried out to evaluate the impact of RFI on the Square Kilometre Array, an international effort to build the largest most sensitive radio telescope.
{"title":"A Framework for RFI Simulation and Performance Verification","authors":"G. Hovey, Federico Di Vruno","doi":"10.23919/RFI48793.2019.9111822","DOIUrl":"https://doi.org/10.23919/RFI48793.2019.9111822","url":null,"abstract":"Modern radio telescopes, like the proposed Square Kilometre Array (SKA), are extremely sensitive and the faint signals they receive can easily be contaminated irreversibly by stray radio frequency interference (RFI). Understanding how radio telescope performance is degraded by RFI is important. In this paper we describe an RFI simulation framework that can be used to generate test stimulus and verify a telescope’s performance. The framework can be used during design to investigate the impact of various RFI scenarios and develop mitigation strategies. As well, it can be used to exercise and test hardware firmware after a system is installed. A prototype of the framework was implemented in the Python computer language to demonstrate the key concepts. Additionally, we outline the framework requirements, describe a suitable software structure and discuss a prototype implementation. As well, we present measurements made to verify the software generates correct test stimulus, for RFI from aircraft distance measuring equipment (DME). The work described was carried out to evaluate the impact of RFI on the Square Kilometre Array, an international effort to build the largest most sensitive radio telescope.","PeriodicalId":111866,"journal":{"name":"2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124603130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.23919/RFI48793.2019.9111652
Y. Soldo, D. L. Le Vine, P. de Matthaeis
Radio Frequency Interference (RFI) is a well-documented problem for passive remote sensing of the Earth at L-band even though the measurements are made in the protected band centered at 1.413 GHz. Consequently, filtering for RFI is an important early step in the processing of measurements made by the SMAP (Soil Moisture Active/Passive) radiometer. However, the filtered data still include regions with suspiciously high antenna temperatures. One possible cause of these “hot spots” is interference not fully detected during RFI filtering. This paper presents some evidence supporting this hypothesis and describes an algorithm to identify these “hot spots” so that they can be removed from the measurements. The impact of removing these “hot spots” is generally small, but evidence is presented that the brightness temperature and soil moisture improve when the hot spots are removed.
{"title":"An Approach to Address Residual “Hot Spots” in SMAP RFI-Filtered Data","authors":"Y. Soldo, D. L. Le Vine, P. de Matthaeis","doi":"10.23919/RFI48793.2019.9111652","DOIUrl":"https://doi.org/10.23919/RFI48793.2019.9111652","url":null,"abstract":"Radio Frequency Interference (RFI) is a well-documented problem for passive remote sensing of the Earth at L-band even though the measurements are made in the protected band centered at 1.413 GHz. Consequently, filtering for RFI is an important early step in the processing of measurements made by the SMAP (Soil Moisture Active/Passive) radiometer. However, the filtered data still include regions with suspiciously high antenna temperatures. One possible cause of these “hot spots” is interference not fully detected during RFI filtering. This paper presents some evidence supporting this hypothesis and describes an algorithm to identify these “hot spots” so that they can be removed from the measurements. The impact of removing these “hot spots” is generally small, but evidence is presented that the brightness temperature and soil moisture improve when the hot spots are removed.","PeriodicalId":111866,"journal":{"name":"2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121268554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.23919/RFI48793.2019.9111709
Qi Liu, Ye Liu, Mao-zheng Chen, X. Su, Feng Liu, Na Wang, Yong Zhang, Ming Zhang, Shipeng Zhang
The proposed Qi Tai 110m radio Telescope (QTT) is a fully steerable single-dish radio telescope with an observing frequency coverage from 150 MHz to 115 GHz. The QTT will play an important role for fundamental research fields of radio astronomy, such as pulsars, molecular spectral lines, active galactic nuclei, and VLBI observations [1]. In order to mitigate the potential interference in the buildings at the QTT site, a low-cost shielding scheme is proposed for the buildings, to mitigate medium and low level RFIs. The measured shielding effectiveness shows that the scheme achieves our design goal.
{"title":"Shielding Engineering Progress for the QTT Buildings","authors":"Qi Liu, Ye Liu, Mao-zheng Chen, X. Su, Feng Liu, Na Wang, Yong Zhang, Ming Zhang, Shipeng Zhang","doi":"10.23919/RFI48793.2019.9111709","DOIUrl":"https://doi.org/10.23919/RFI48793.2019.9111709","url":null,"abstract":"The proposed Qi Tai 110m radio Telescope (QTT) is a fully steerable single-dish radio telescope with an observing frequency coverage from 150 MHz to 115 GHz. The QTT will play an important role for fundamental research fields of radio astronomy, such as pulsars, molecular spectral lines, active galactic nuclei, and VLBI observations [1]. In order to mitigate the potential interference in the buildings at the QTT site, a low-cost shielding scheme is proposed for the buildings, to mitigate medium and low level RFIs. The measured shielding effectiveness shows that the scheme achieves our design goal.","PeriodicalId":111866,"journal":{"name":"2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI)","volume":"37 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131084929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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