Radio Frequency Interference Identification Research Articles

Radio frequency interference identification using dual cross-attention and multi-scale feature fusing

Radio astronomy plays a very important role in promoting scientific progress and unraveling the mysteries of the universe. However, radio telescopes are inevitably affected by radio frequency interference (RFI) when receiving radio signals, which leads to a reduction in data quality and has a serious impact on the formation of correct scientific conclusions. Therefore, it is essential to identify the RFI present in the observational data. In order to effectively identify RFI, improve the existing RFI identification methods that suffer from missed detections, and enhance the performance of RFI identification, this paper proposes a novel method that combines a dual cross-attention mechanism with multi-scale feature fusion. Experimental studies were conducted using the observational data from the 40-meter radio telescope at the Yunnan Astronomical Observatory of the Chinese Academy of Sciences. The proposed method achieved scores of 92.49%, 83.90%, and 87.99% in terms of precision, recall, and F1−score, respectively. It outperformed existing methods (U-Net, RFI-Net, R-Net6, RFI-GAN, EMSCA-UNet) in recall and F1−score, effectively reducing the occurrence of missed detections and improving the overall performance of radio frequency interference identification.

Identification and Correction of Radio Frequency Interference of Fengyun-3 Microwave Radiation Imager Using a Machine-Learning Method

Radio frequency signals can interfere with the radiation emanated from the earth atmospheres and affect the quality of the data received from spaceborne microwave instruments. For microwave radiation imager (MWRI) carried on China’s Fengyun-3 series satellites, the data contaminated by radio frequency interference (RFI) are usually identified and labeled as poor quality. In this study, using the high correlation between the observed brightness temperatures (TB) of MWRI channels, an RFI identification and correction method is developed through machine learning techniques. Compared with traditional methods, the new method can simultaneously identify and correct RFI affected data. Since it is trained with global MWRI data, the method works well for both land and oceans. Our analysis show that the MWRI data affected by RFI can be corrected to the quality level close to RFI-free regions.

Scalable framework of intelligent RFI flagging for large-scale HI survey data from FAST

Radio frequency interference (RFI) identification is a key step in radio data processing. In order to efficiently process huge volumes of data produced by modern large radio telescopes, such as the Five-hundred-meter Aperture Spherical radio Telescope (FAST), exceptional balance between accuracy and performance (throughput) is required for RFI flagging algorithms. RFI-Net is a single-process RFI identification package based on deep learning technique, and has achieved a higher flagging accuracy than the classical SumThreshold method. In this paper, we present a scalable RFI flagging toolkit, which can drive parallel workflows on multi-CPU and multi-GPU clusters, with RFI-Net as its core detector. It can automatically schedule the workload and aggregate itself after errors according to the running environment. Moreover, its main components are all pluggable, and can be easily customized according to requirements. The experiments with real data of FAST showed that using eight parallel workflows, the toolkit can process sky survey data at a speed of 66.79 GB/h, which means quasi-real-time RFI flagging can be achieved considering the data rate of FAST extragalactic spectral line observations.

A robust RFI identification for radio interferometry based on a convolutional neural network

ABSTRACTThe rapid development of new generation radio interferometers such as the Square Kilometer Array (SKA) has opened up unprecedented opportunities for astronomical research. However, anthropogenic radio frequency interference (RFI) from communication technologies and other human activities severely affects the fidelity of observational data. It also significantly reduces the sensitivity of the telescopes. We proposed a robust convolutional neural network (CNN) model to identify RFI based on machine-learning methods. We overlaid RFI on the simulation data of SKA1-LOW to construct three visibility function data sets. One data set was used for modelling, and the other two were used for validating the model’s usability. The experimental results show that the area under the curve reaches 0.93, with satisfactory accuracy and precision. We then further investigated the effectiveness of the model by identifying the RFI in the actual observational data from LOFAR and MeerKAT. The results show that the model performs well. The overall effectiveness is comparable to AOFlagger software and provides an improvement over existing methods in some instances.

DSC based Dual-Resunet for radio frequency interference identification

Radio frequency interference (RFI) will pollute the weak astronomical signals receivedby radio telescopes, which in return will seriously affect the time-domain astronomical observation and research. In this paper, we use a deep learning method to identify RFI in frequency spectrum data, and propose a neural network based on Unet that combines the principles of depthwise separable convolution and residual, named DSC Based Dual-Resunet. Compared with the existing Unet network, DSC Based Dual-Resunet performs better in terms ofaccuracy, F1 score, and MIoU, and is also better in terms of computation cost where the model size and parameter amount are 12.5% of Unet and the amount of computation is 38%ofUnet. The experimental results show that the proposed network is a high-performance and lightweight network, and it is hopeful to be applied to RFI identification of radio telescopes on a large scale.

Detection of radio frequency interference using an improved generative adversarial network

Radio Frequency Interference (RFI) is a type of inevitable noise in the radio astronomy data collection process. It can corrupt weak cosmic signals and potentially lead to misleading results. The proper identification of RFI during data processing thus is critical to obtain clean and high-quality data for analysis. This need will become even more urgent when next generation of large radio telescopes, such as the Five-hundred-meter Aperture Spherical radio Telescope (FAST) comes into service and generates an increasing amount and complexity of radio signal data. Among RFI identification methods, detection using artificial intelligence (AI) has particularly demonstrated advantages in superior efficiency, accuracy and less human intervention. We thus propose a RFI detection model based on Pix2Pix , an image-to-image translation solver using Generative Adversarial Network (GAN): RFI-GAN. This model transforms the RFI detection to an image translation problem and trains two deep neural networks that contest each other to output a binary RFI mask image to eliminate RFI noises. We also optimize the network structures of the generator and discriminator used in the Pix2Pix model for better quality of RFI detection, making it suitable for processing data from single antenna radio telescope. The model is designed to serve the upcoming FAST data and has been evaluated using a standard simulation data set generated by the HI Data Emulator ( HIDE ). Experimental results have shown that our model can achieve higher scores (99%) on accuracy, recall and F1-score than other RFI detection methods.

RFI Suppression for SAR Systems Based on Removed Spectrum Iterative Adaptive Approach

A synthetic aperture radar (SAR) system can be seriously contaminated by radio frequency systems because of working in the same microwave frequency bands, which would degrade the SAR image quality and affect the accuracy of image interpretation. In this paper, a novel radio frequency interference (RFI) suppression approach including RFI identification, band-stop filtering and a removed spectrum iterative adaptive approach (RSIAA) is proposed. First, the smoothing process is added before RFI signal detection to improve the RFI detection capacity. Afterwards, the band-stop filtering with a broaden factor is proposed to mitigate the residual RFI, and it ensures the accuracy of the following removed spectrum recovery by the RSIAA. Finally, the removed spectrum components are estimated from available adjacent spectrum data by the RSIAA in turn to obtain the desired range spectra. Compared with the conventional range frequency filtering method for RFI suppression, the capacity of the weak RFI signal detection is improved, and the increased sidelobes due to the discontinuous spectra are well suppressed. Simulation experiments on both simulated SAR raw data, Gaofen-3 and Sentinel-1 SAR raw data validate the proposed RFI suppression approach.

Cross-Validation of Radio-Frequency-Interference Signature in Satellite Microwave Radiometer Observations over the Ocean

Radio-frequency-interference (RFI) signals have gradually become a more serious problem in active and passive microwave remote sensing. However, currently, there is no reliable RFI source distribution data to evaluate the accuracy of existing RFI identification methods. In this study, a simplified generalized RFI detection method (GRDM) is proposed to detect RFI applied to the ocean surface. Two RFI detection methods, the GRDM and the double-principal component analysis (DPCA) method, are used for cross-validation to obtain RFI recognition thresholds of DPCA in the Advanced Microwave Scanning Radiometer 2 (AMSR2) ocean data. In addition, in the present work the source and distribution characteristics of RFI over the ocean surface are also analyzed. The results show that the proposed scheme can effectively identify RFI signals from AMSR2 data, and only 7.3, 10.65, and 18.7 GHz channels are contaminated by RFI over the ocean surface. There are strong 7.3 GHz interference signals over the waters of East Asia (with the value of ΔTBH mostly between 5 and 30 K and ΔTBv mostly between 5 and 40 K), Europe (with the value of ΔTBH mostly between 5 and 40 K and ΔTBv mostly between 5 and 30 K), and North America (with the value of ΔTBH mostly between 5 and 50 K and ΔTBv mostly between 5 and 30 K). The RFI signals in 10.65 GHz data are mainly distributed over the Mediterranean and other European waters (with the value of ΔTBH mostly between 5 and 35 K and ΔTBv mostly between 5 and 20 K). The RFI signals at 18.7 GHz are mainly present over the offshore marine areas of North America (with the value of ΔTBH mostly between 5 and 50 K and ΔTBv mostly between 5 and 40 K), with the strongest RFI distributed near the Great Lakes of America, and the RFI magnitudes over the east and west coasts are stronger than over the south coast. Satellite-borne microwave observations over the ocean suffer from interference mainly from stationary communication/television satellites. Due to the reflection of the sea surface, the range and intensity of RFI are strongly dependent on the relative geometric positions of stationary satellites and space-borne passive instruments. Therefore, RFI coverage area changes every day over the ocean in one 16-day period, which is very different from that over the land.

A Dictionary Approach to Identifying Transient RFI

AbstractAs radio telescopes become more sensitive, the damaging effects of radio frequency interference (RFI) become more apparent. One particular approach to mitigating RFI is to deploy independent, omnidirectional wideband monitoring systems at radio telescope arrays. Near such radio telescopes, detected RFI sources are often easily removed or replaced; the challenge lies in identifying them. Transient (impulsive) RFI is particularly difficult to identify. We propose a novel dictionary‐based approach to transient RFI identification. RFI events are treated as sequences of subevents, drawn from particular labeled classes. We demonstrate an automated method of extracting and labeling subevents using a data set of transient RFI. A dictionary of labels may be used in conjunction with hidden Markov models to identify the sources of RFI events reliably. We attain improved classification accuracy over traditional approaches such as support vector machines or a naïve k‐Nearest Neighbor classifier. Finally, we investigate why transient RFI is difficult to classify. We show that cluster separation in the principal components domain is influenced by the alternating current supply phase for certain sources.

The radio environment of the 21 Centimeter Array: RFI detection and mitigation

Detection and mitigation of radio frequency interference (RFI) is the first and also the key step for data processing in radio observations, especially for ongoing low frequency radio experiments towards the detection of the cosmic dawn and epoch of reionization (EoR). In this paper we demonstrate the technique and efficiency of RFI identification and mitigation for the 21 Centimeter Array (21CMA), a radio interferometer dedicated to the statistical measurement of EoR. For terrestrial, man-made RFI, we concentrate mainly on a statistical approach by identifying and then excising non-Gaussian signatures, in the sense that the extremely weak cosmic signal is actually buried under thermal and therefore Gaussian noise. We also introduce the so-called visibility correlation coefficient instead of conventional visibility, which allows a further suppression of rapidly time-varying RFI. Finally, we briefly discuss removals of the sky RFI, the leakage of sidelobes from off-field strong radio sources with time-invariant power and a featureless spectrum. It turns out that state of the art technique should allow us to detect and mitigate RFI to a satisfactory level in present low frequency interferometer observations such as those acquired with the 21CMA, and the accuracy and efficiency can be greatly improved with the employment of low-cost, high-speed computing facilities for data acquisition and processing.

Statistical Hypothesis Testing and Variance Analysis for Radio Frequency Interference Identification in Solar Data

ABSTRACTThis work presents an effective algorithm for radio frequency interference (RFI) identification using dynamic power spectrum statistics in the frequency domain. Statistical signal processing techniques such as hypothesis testing and variance analysis are utilized to derive a test statistic for effective and efficient RFI identification. Starting from the generalized likelihood ratio test (GLRT), we formulate the problem systematically and propose a practical test statistic T(x; f), shown to be distributed, for RFI identification. A threshold approach working on this test statistic is developed to identify the presence of narrowband RFI in the power spectrum with additive Gaussian noise and/or solar flare background, corresponding to a desired constant false alarm rate (CFAR). Detailed analysis on detector performance and effect of RFI duty cycle are also provided. The proposed statistical test is applied to experimental solar data collected by our frequency-agile solar radio telescope (FASR) subsystem testbed (FST) to demonstrate the robustness and scalability of the algorithm, as well as its capability for real-time implementation.

Radio frequency interference identification and mitigation using simultaneous dual‐station observations

Radio frequency interference (RFI) mitigation is a critically important issue in radio astronomy using existing instruments as well as in the development of next generation radio telescopes, such as the Square Kilometer Array (SKA). Most designs for the SKA involve multiple stations with spacings of up to a few thousands of kilometers and thus can exploit the drastically different RFI environments at different stations. As demonstrator observations and analysis for SKA‐like instruments, and to develop RFI mitigation schemes that will be useful in the near term, we recently conducted simultaneous observations with Arecibo Observatory and the Green Bank Telescope. The observations were aimed at diagnosing RFI and using the mostly uncorrelated RFI between the two sites to excise RFI from several generic kinds of measurements such as giant pulses from Crab‐like pulsars and weak H I emission from galaxies in bands heavily contaminated by RFI. This paper presents observations, analysis, and RFI identification and excision procedures that are effective for both time series and spectroscopy applications using multistation data.