Radio Frequency Interference (RFI) is unwanted noise that swamps the desired astronomical signal. Radio astronomers have always had to deal with RFI detection and excision around telescope sites, but little has been done to understand the full scope, nature and evolution of RFI in a unified way. We undertake this for the MeerKAT array using a probabilistic multidimensional framework approach focussing on UHF-band and L-band data. In the UHF- band, RFI is dominated by the allocated Global System for Mobile (GSM) Communications, flight Distance Measuring Equipment (DME), and UHF-TV bands. The L-band suffers from known RFI sources such as DMEs, GSM, and the Global Positioning System (GPS) satellites. In the "clean" MeerKAT band, we noticed the RFI occupancy changing with time and direction for both the L-band and UHF band. For example, we saw a significant increase (300% increase) in the fraction of L-band flagged data in November 2018 compared to June 2018. This increase seems to correlate with construction activity on site. In the UHF-band, we found that the early morning is least impacted by RFI and other outliers. We also found a dramatic decrease in DME RFI during the hard lockdown due to the COVID-19 pandemic. The work presented here allows us to characterise the evolution of RFI at the MeerKAT site. Any observatory can adopt it to understand the behaviour of RFI within its surroundings.
{"title":"Nature and Evolution of UHF and L-band Radio Frequency Interference at the MeerKAT Radio Telescope","authors":"Isaac Sihlangu, N. Oozeer, Bruce A. Bassett","doi":"10.46620/rfi22-003","DOIUrl":"https://doi.org/10.46620/rfi22-003","url":null,"abstract":"Radio Frequency Interference (RFI) is unwanted noise that swamps the desired astronomical signal. Radio astronomers have always had to deal with RFI detection and excision around telescope sites, but little has been done to understand the full scope, nature and evolution of RFI in a unified way. We undertake this for the MeerKAT array using a probabilistic multidimensional framework approach focussing on UHF-band and L-band data. In the UHF- band, RFI is dominated by the allocated Global System for Mobile (GSM) Communications, flight Distance Measuring Equipment (DME), and UHF-TV bands. The L-band suffers from known RFI sources such as DMEs, GSM, and the Global Positioning System (GPS) satellites. In the \"clean\" MeerKAT band, we noticed the RFI occupancy changing with time and direction for both the L-band and UHF band. For example, we saw a significant increase (300% increase) in the fraction of L-band flagged data in November 2018 compared to June 2018. This increase seems to correlate with construction activity on site. In the UHF-band, we found that the early morning is least impacted by RFI and other outliers. We also found a dramatic decrease in DME RFI during the hard lockdown due to the COVID-19 pandemic. The work presented here allows us to characterise the evolution of RFI at the MeerKAT site. Any observatory can adopt it to understand the behaviour of RFI within its surroundings.","PeriodicalId":186234,"journal":{"name":"Proceedings for RFI 2022","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116621819","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}
{"title":"VHF antenna calibration for RFI monitoring using sky signal","authors":"","doi":"10.46620/rfi22-007","DOIUrl":"https://doi.org/10.46620/rfi22-007","url":null,"abstract":"","PeriodicalId":186234,"journal":{"name":"Proceedings for RFI 2022","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124748646","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}
We investigate the effectiveness of the statistical radio frequency interference (RFI) mitigation tech-nique spectral kurtosis ( (cid:100) SK ) in the face of simulated realistic RFI signals. (cid:100) SK estimates the kurtosis of a collection of M power values in a single channel and provides a detection metric that is able to discern between human-made RFI and incoherent astronomical signals of interest. We test the ability of (cid:100) SK to flag signals with various representative modulation types, data rates, duty cycles, and carrier frequencies. We flag with various accumulation lengths M and implement multi-scale (cid:100) SK , which com-bines information from adjacent time-frequency bins to mitigate weaknesses in single-scale (cid:100) SK . We find that signals with significant sidelobe emission from high data rates are harder to flag, as well as signals with a 50% effective duty cycle and weak signal-to-noise ratios. Multi-scale (cid:100) SK with at least one extra channel can detect both the center channel and side-band interference, flagging greater than 90% as long as the bin channel width is wider in frequency than the RFI.
{"title":"Simulating Spectral Kurtosis Mitigation against Realistic RFI signals","authors":"Evan T. Smith, R. Lynch, D. Pisano","doi":"10.46620/rfi22-010","DOIUrl":"https://doi.org/10.46620/rfi22-010","url":null,"abstract":"We investigate the effectiveness of the statistical radio frequency interference (RFI) mitigation tech-nique spectral kurtosis ( (cid:100) SK ) in the face of simulated realistic RFI signals. (cid:100) SK estimates the kurtosis of a collection of M power values in a single channel and provides a detection metric that is able to discern between human-made RFI and incoherent astronomical signals of interest. We test the ability of (cid:100) SK to flag signals with various representative modulation types, data rates, duty cycles, and carrier frequencies. We flag with various accumulation lengths M and implement multi-scale (cid:100) SK , which com-bines information from adjacent time-frequency bins to mitigate weaknesses in single-scale (cid:100) SK . We find that signals with significant sidelobe emission from high data rates are harder to flag, as well as signals with a 50% effective duty cycle and weak signal-to-noise ratios. Multi-scale (cid:100) SK with at least one extra channel can detect both the center channel and side-band interference, flagging greater than 90% as long as the bin channel width is wider in frequency than the RFI.","PeriodicalId":186234,"journal":{"name":"Proceedings for RFI 2022","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132859855","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}
{"title":"Potential Impacts of Radio Frequency Interference on precipitation retrievals from space – from an IPWG perspective","authors":"","doi":"10.46620/rfi22-004","DOIUrl":"https://doi.org/10.46620/rfi22-004","url":null,"abstract":"","PeriodicalId":186234,"journal":{"name":"Proceedings for RFI 2022","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120831978","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}
{"title":"Real time RFI monitoring for Radio Telescope array","authors":"","doi":"10.46620/rfi22-002","DOIUrl":"https://doi.org/10.46620/rfi22-002","url":null,"abstract":"","PeriodicalId":186234,"journal":{"name":"Proceedings for RFI 2022","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126889045","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}
{"title":"Characterization of the RFI Environment at the DRAO: The Classical Approach","authors":"","doi":"10.46620/rfi22-012","DOIUrl":"https://doi.org/10.46620/rfi22-012","url":null,"abstract":"","PeriodicalId":186234,"journal":{"name":"Proceedings for RFI 2022","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115543175","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}
features of targets in the images and further be employed to classify different targets. Previous methods, such as the deep convolutional neural network (DCNN), were used to mitigate both narrowband and wideband interferences in SAR images (W. Fan, F. Zhou, et. al., Remote Sens., 11(14), 1654, 2019). For the unsupervised learning frameworks, such as auto-encoder (Y. Wang, H. Yao, et. al., Neurocomputing, 184, 232-242, 2016) and generative adversarial network (GAN) (I. Goodfellow, J. Pouget-Abadi, M. Mirza, et al, Advances in neural information processing systems, 27, 2014), they can discover naturally intrinsic property of the data without any pre-assigned labels and are widely leveraged in the image denoising area. Also, the PCA-based unsupervised learning methods (M. Tao, J. Su, et. al, Remote Sens., 11, 2438, 2019) are widely used to mitigate RFIs in the corrupted SAR data. Therefore, we come out an idea that the unsupervised learning frameworks have great potential on separate the RFI and the useful data. In this paper, we use an auto-encoder framework, combining real and imaginary parts of complex radar data as two branches, to mitigate strong RFIs from corrupted SAR data. It brings another perspective for RFI mitigation via deep unsupervised learning approach.
利用图像中目标的特征，进一步对不同目标进行分类。以前的方法，如深度卷积神经网络(DCNN)，被用来减轻SAR图像中的窄带和宽带干扰(范伟，周峰等，遥感，11(14)，1654,2019)。对于无监督学习框架，如自编码器(Y. Wang, H. Yao, et al.， Neurocomputing, 184, 232-242, 2016)和生成式对抗网络(GAN) (I. Goodfellow, J. Pouget-Abadi, M. Mirza, et al.， Advances in neural information processing systems, 27, 2014)，它们可以在没有任何预先分配标签的情况下发现数据的自然固有属性，并广泛用于图像去干扰领域。此外，基于pca的无监督学习方法(M. Tao, J. Su, et al .， Remote Sens.， 11, 2438, 2019)被广泛用于减轻损坏SAR数据中的rfi。因此，我们认为无监督学习框架在分离RFI和有用数据方面具有很大的潜力。在本文中，我们使用一个自动编码器框架，结合复杂雷达数据的实部和虚部作为两个分支，以减轻来自损坏SAR数据的强rfi。它为通过深度无监督学习方法缓解RFI带来了另一种视角。
{"title":"RFI Mitigation via Auto-Encoder Framework in SAR Data","authors":"Jiang Liu, Yunxuan Wang, Yuan Mao, Junli Chen, Yanyang Liu, Yan Huang","doi":"10.46620/rfi22-013","DOIUrl":"https://doi.org/10.46620/rfi22-013","url":null,"abstract":"features of targets in the images and further be employed to classify different targets. Previous methods, such as the deep convolutional neural network (DCNN), were used to mitigate both narrowband and wideband interferences in SAR images (W. Fan, F. Zhou, et. al., Remote Sens., 11(14), 1654, 2019). For the unsupervised learning frameworks, such as auto-encoder (Y. Wang, H. Yao, et. al., Neurocomputing, 184, 232-242, 2016) and generative adversarial network (GAN) (I. Goodfellow, J. Pouget-Abadi, M. Mirza, et al, Advances in neural information processing systems, 27, 2014), they can discover naturally intrinsic property of the data without any pre-assigned labels and are widely leveraged in the image denoising area. Also, the PCA-based unsupervised learning methods (M. Tao, J. Su, et. al, Remote Sens., 11, 2438, 2019) are widely used to mitigate RFIs in the corrupted SAR data. Therefore, we come out an idea that the unsupervised learning frameworks have great potential on separate the RFI and the useful data. In this paper, we use an auto-encoder framework, combining real and imaginary parts of complex radar data as two branches, to mitigate strong RFIs from corrupted SAR data. It brings another perspective for RFI mitigation via deep unsupervised learning approach.","PeriodicalId":186234,"journal":{"name":"Proceedings for RFI 2022","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121585124","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}
{"title":"On the THERMOpYlae Hellenic radio telescope radio silent Site","authors":"","doi":"10.46620/rfi22-016","DOIUrl":"https://doi.org/10.46620/rfi22-016","url":null,"abstract":"","PeriodicalId":186234,"journal":{"name":"Proceedings for RFI 2022","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116758032","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}
{"title":"Introducing Machine Learning in the Ground RFI Detection System (GRDS)","authors":"","doi":"10.46620/rfi22-008","DOIUrl":"https://doi.org/10.46620/rfi22-008","url":null,"abstract":"","PeriodicalId":186234,"journal":{"name":"Proceedings for RFI 2022","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128890824","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}
Radio Frequency Interference (RFI) is a hindrance to high-precision pulsar timing experiments aimed at detecting the stochastic gravitational wave background. Thresholds set by linear combinations of statistical quantities are among the most common approaches to RFI flagging of folded pulse profiles. We propose a deep convolutional neural network approach to RFI flagging called PSRFINET that treats two-dimensional arrays of pulse profiles (rotational phase versus radio frequency) as images and performs feature learning on labelled RFI samples. We train and validate multiple deep residual neural networks on many hours of pulsar observations (thousands of 8 second sub-integrations) of MeerKAT L-band data where the ground truth is generated from Clfd and Coastguard software packages for RFI mitigation. A method of combining the separate ground truths aimed at enhancing the RFI mitigation capabilities of the networks is also explored. The performance of the networks was evaluated by examining the classification metrics of area under the curve of the receiver operating characteristic (AUROC), Precision-Recall (PR) and F1 scores. Our preliminary results show an AUROC of more than 0.91 and PR of 0.67 which indicates that although the neural networks are capable of distinguishing between clean and corrupted frequency channels, precision and recall scores are limited by a class imbalance of a small amount of RFI with respect to clean channels. We also discuss our approach to develop a statistical objective figure of merit for evaluating and comparing the effectiveness of different RFI flagging approaches in the data.
{"title":"PSRFINET: Radio Frequency Interference Detection in Pulsar Data with Deep Residual Networks","authors":"A. Hamid, W. Straten, A. Griffin","doi":"10.46620/rfi22-005","DOIUrl":"https://doi.org/10.46620/rfi22-005","url":null,"abstract":"Radio Frequency Interference (RFI) is a hindrance to high-precision pulsar timing experiments aimed at detecting the stochastic gravitational wave background. Thresholds set by linear combinations of statistical quantities are among the most common approaches to RFI flagging of folded pulse profiles. We propose a deep convolutional neural network approach to RFI flagging called PSRFINET that treats two-dimensional arrays of pulse profiles (rotational phase versus radio frequency) as images and performs feature learning on labelled RFI samples. We train and validate multiple deep residual neural networks on many hours of pulsar observations (thousands of 8 second sub-integrations) of MeerKAT L-band data where the ground truth is generated from Clfd and Coastguard software packages for RFI mitigation. A method of combining the separate ground truths aimed at enhancing the RFI mitigation capabilities of the networks is also explored. The performance of the networks was evaluated by examining the classification metrics of area under the curve of the receiver operating characteristic (AUROC), Precision-Recall (PR) and F1 scores. Our preliminary results show an AUROC of more than 0.91 and PR of 0.67 which indicates that although the neural networks are capable of distinguishing between clean and corrupted frequency channels, precision and recall scores are limited by a class imbalance of a small amount of RFI with respect to clean channels. We also discuss our approach to develop a statistical objective figure of merit for evaluating and comparing the effectiveness of different RFI flagging approaches in the data.","PeriodicalId":186234,"journal":{"name":"Proceedings for RFI 2022","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124547910","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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