Related papers: An Improved Machine Learning Approach for Radio Fr…
Signal artefacts due to Radio Frequency Interference (RFI) are a common nuisance in radio astronomy. Conventionally, the RFI-affected data are tagged by an expert data analyst in order to warrant data quality. In view of the increasing data…
We investigate the effect of radio-frequency interference (RFI) excision in estimating the cosmological \hi\ 21 cm power spectrum. Flagging of RFI-contaminated channels results in a non-uniform sampling of the instrumental bandpass…
We introduce a new pipeline for analyzing and mitigating radio frequency interference (RFI), which we call Sky-Subtracted Incoherent Noise Spectra (SSINS). SSINS is designed to identify and remove faint RFI below the single baseline thermal…
The 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…
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…
Increasing levels of Radio Frequency Interference (RFI) are a problem for research in radio astronomy. Various techniques to suppress RFI and extract astronomical signals from data affected by interference are being tried out. However,…
Machine learning research in GNSS radio frequency interference (RFI) detection often lacks a clear empirical justification for the choice of deep learning architectures over simpler machine learning approaches. In this work, we argue for a…
Long baseline radio interferometers can provide some interesting opportunities for future SETI searches. Known advantages (compared to single dishes or beam-formed arrays), include the large reduction in false-positives due to the…
This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using WiFi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver…
One of the challenges in spaceborne synthetic aperture radar (SAR) is modeling and mitigating radio frequency interference (RFI) artifacts in SAR imagery. Linear frequency modulated (LFM) signals have been commonly used for characterizing…
With the ongoing growth in radio communications, there is an increased contamination of radio astronomical source data, which hinders the study of celestial radio sources. In many cases, fast mitigation of strong radio frequency…
Radio Frequency Interference (RFI) from anthropogenic radio sources poses significant challenges to current and future radio telescopes. Contemporary approaches to detecting RFI treat the task as a semantic segmentation problem on radio…
In neutral hydrogen (HI) galaxy survey, a significant challenge is to identify and extract the HI galaxy signal from observational data contaminated by radio frequency interference (RFI). For a drift-scan survey, or more generally a survey…
The Giant Metrewave Radio Telescope (GMRT) is being upgraded to increase the receiver sensitivity. This makes the receiver more susceptible to man-made Radio Frequency Interference (RFI). To improve the receiver performance in presence of…
Traditionally, fast radio transient searches are conducted on dedispersed time series using thresholding techniques based on the statistical properties of the data. However, peaks in dedispersed time series do not directly provide…
The growing need for electromagnetic spectrum to support the next generation (xG) communication networks increasingly generate unwanted radio frequency interference (RFI) in protected bands for radio astronomy. RFI is commonly mitigated at…
Radio interferometers are phased arrays producing high-resolution images from the covariance matrix of measurements. Calibration of such instruments is necessary and is a critical task. This is how the estimation of instrumental errors is…
We present two algorithms to identify and flag radio frequency interference (RFI) in radio interferometric imaging data. The first algorithm utilizes the redundancy of visibilities inside a UV cell in the visibility plane to identify…
Contamination by Radio Frequency Interference (RFI) is a ubiquitous challenge for radio astronomy. In particular, transient RFI is difficult to detect and avoid, especially in large data sets with many time bins. In this work, we present a…
Flagging of Radio Frequency Interference (RFI) is an increasingly important challenge in radio astronomy. We present R-Net, a deep convolutional ResNet architecture that significantly outperforms existing algorithms -- including the default…