Asset Details
Do you wish to reserve the book?
Interference detection in radio astronomy applying Shapiro-Wilks normality test, spectral entropy, and spectral relative entropy
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Interference detection in radio astronomy applying Shapiro-Wilks normality test, spectral entropy, and spectral relative entropy
Do you wish to request the book?
Interference detection in radio astronomy applying Shapiro-Wilks normality test, spectral entropy, and spectral relative entropy
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Paper
Interference detection in radio astronomy applying Shapiro-Wilks normality test, spectral entropy, and spectral relative entropy
2024
Overview
Radio-frequency interference (RFI) is becoming an increasingly significant problem for most radio telescopes. Working with Green Bank Telescope data from PSR J1730+0747 in the form of complex-valued channelized voltages and their respective high-resolution power spectral densities, we evaluate a variety of statistical measures to characterize RFI. As a baseline for performance comparison, we use median absolute deviation (MAD) in complex channelized voltage data and spectral kurtosis (SK) in power spectral density data to characterize and filter out RFI. From a new perspective, we implement the Shapiro-Wilks (SW) test for normality and two information theoretical measures, spectral entropy (SE) and spectral relative entropy (SRE), and apply them to mitigate RFI. The baseline RFI mitigation algorithms are compared against our novel RFI detection algorithms to determine how effective and robust the performance is. Except for MAD, we find significant improvements in signal-to-noise ratio through the application of SE, symmetrical SRE, asymmetrical SRE, SK, and SW. These algorithms also do a good job of characterizing broadband RFI. Time- and frequency-variable RFI signals are best detected by SK and SW tests.
Publisher
Cornell University Library, arXiv.org
