Proceedings for RFI 2022最新文献

Nature and Evolution of UHF and L-band Radio Frequency Interference at the MeerKAT Radio Telescope MeerKAT射电望远镜上UHF和l波段射频干扰的性质和演变

Proceedings for RFI 2022 Pub Date : 2022-11-16 DOI: 10.46620/rfi22-003

Isaac Sihlangu, N. Oozeer, Bruce A. Bassett

{"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}

引用次数: 1

VHF antenna calibration for RFI monitoring using sky signal 使用天空信号进行射频信号监测的甚高频天线校准

Proceedings for RFI 2022 Pub Date : 1900-01-01 DOI: 10.46620/rfi22-007

引用次数: 0

Simulating Spectral Kurtosis Mitigation against Realistic RFI signals 模拟实际RFI信号的频谱峰度缓解

Proceedings for RFI 2022 Pub Date : 1900-01-01 DOI: 10.46620/rfi22-010

Evan T. Smith, R. Lynch, D. Pisano

引用次数: 0

Potential Impacts of Radio Frequency Interference on precipitation retrievals from space – from an IPWG perspective 射频干扰对空间降水检索的潜在影响——从IPWG的角度

Proceedings for RFI 2022 Pub Date : 1900-01-01 DOI: 10.46620/rfi22-004

引用次数: 0

Real time RFI monitoring for Radio Telescope array 射电望远镜阵列实时RFI监测

Proceedings for RFI 2022 Pub Date : 1900-01-01 DOI: 10.46620/rfi22-002

引用次数: 0

Characterization of the RFI Environment at the DRAO: The Classical Approach DRAO中RFI环境的表征:经典方法

Proceedings for RFI 2022 Pub Date : 1900-01-01 DOI: 10.46620/rfi22-012

引用次数: 0

RFI Mitigation via Auto-Encoder Framework in SAR Data 通过SAR数据中的自动编码器框架缓解RFI

Proceedings for RFI 2022 Pub Date : 1900-01-01 DOI: 10.46620/rfi22-013

Jiang Liu, Yunxuan Wang, Yuan Mao, Junli Chen, Yanyang Liu, Yan Huang

{"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}

引用次数: 0

On the THERMOpYlae Hellenic radio telescope radio silent Site 在THERMOpYlae上，希腊射电望远镜射电静默站点

Proceedings for RFI 2022 Pub Date : 1900-01-01 DOI: 10.46620/rfi22-016

引用次数: 0

Introducing Machine Learning in the Ground RFI Detection System (GRDS) 在地面射频识别系统(GRDS)中引入机器学习

Proceedings for RFI 2022 Pub Date : 1900-01-01 DOI: 10.46620/rfi22-008

引用次数: 0

PSRFINET: Radio Frequency Interference Detection in Pulsar Data with Deep Residual Networks 基于深度残差网络的脉冲星数据射频干扰检测

Proceedings for RFI 2022 Pub Date : 1900-01-01 DOI: 10.46620/rfi22-005

A. Hamid, W. Straten, A. Griffin

{"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}

引用次数: 0