Cyclic Imaging for All-Sky Interference Forecasting with Array Radio Telescopes

TL;DR

This paper introduces a novel method leveraging the cyclostationary properties of RFI signals to detect, predict, and dynamically schedule observations in radio telescopes, reducing interference impact.

Contribution

It presents a new approach using cyclic imaging to forecast RFI interference, enhancing data quality in all-sky radio observations.

Findings

Effective detection of RFI using cyclostationary analysis

Improved scheduling reduces RFI data corruption

Potential for real-time interference mitigation

Abstract

Radio Frequency Interference (RFI) is threatening modern radio astronomy. A classic approach to mitigate its impact on astronomical data involves discarding the corrupted time and frequency data samples through a process called flagging and blanking. We propose the exploitation of the cyclostationary properties of the RFI signals to reliably detect and predict their locations within an array radio telescope field-of-view, and dynamically schedule the astronomical observations such as to minimize the probability of RFI data corruption.