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1. Simulating Spectral Kurtosis Mitigation against Realistic Radio Frequency Interference Signals

   https://doi.org/10.3847/1538-3881/ac7e47  

   Smith, E.; Lynch, Ryan S.; Pisano, D. J. (September 2022, The Astronomical Journal)

   Abstract We investigate the effectiveness of the statistical radio frequency interference (RFI) mitigation technique spectral kurtosis ( $\widehat{\mathit{SK}}$) in the face of simulated realistic RFI signals. $\widehat{\mathit{SK}}$estimates the kurtosis of a collection ofMpower values in a single channel and provides a detection metric that is able to discern between human-made RFI and incoherent astronomical signals of interest. We test the ability of $\widehat{\mathit{SK}}$to flag signals with various representative modulation types, data rates, duty cycles, and carrier frequencies. We flag with various accumulation lengthsMand implement multiscale $\widehat{\mathit{SK}}$, which combines information from adjacent time-frequency bins to mitigate weaknesses in single-scale $\widehat{\mathit{SK}}$. We find that signals with significant sidelobe emission from high data rates are harder to flag, as well as signals with a 50% effective duty cycle and weak signal-to-noise ratios. Multiscale $\widehat{\mathit{SK}}$with at least one extra channel can detect both the center channel and sideband interference, flagging greater than 90% as long as the bin channel width is wider in frequency than the RFI. 

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2. Interference detection in radio astronomy: applying Shapiro–Wilks normality test, spectral entropy, and spectral relative entropy

   https://doi.org/10.1093/rasti/rzae035  

   Cao, Zhicheng; Schmid, Natalia_A; Bandura, Kevin; Lorimer, Duncan_R; Dameron, Morgan; Crockett, Katelyn; Grubick, Clayton; Schmid, Andreas; Zheng, Shaonan (August 2024, RAS Techniques and Instruments)

   Abstract 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 broad-band RFI. Time- and frequency-variable RFI signals are best detected by SK and SW tests. 

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3. Evaluation of Conventional Radio Frequency Interference Detection Algorithms in the Presence of 5G Signals in a Controlled Testbed

   https://doi.org/10.1109/DySPAN60163.2024.10632808  

   Alam, Ahmed Manavi; Farhad, Md Mehedi; AlQwider, Walaa; Marojevic, Vuk; Kurum, Mehmet; Gurbuz, Ali C (May 2024, IEEE)

   Emerging communication services and satellite system deployments pose heightened interference challenges for crucial passive radiometer sensors used in environmental and atmospheric sensing. Therefore, there is an urgent necessity to develop effective approaches for detecting, mitigating, and characterizing the influence of anthropogenic sources, commonly referred to as radio frequency interference (RFI) on passive Earth-observing microwave radiometers. Experimenting the co-existence of active communication and passive sensing systems would greatly benefit from a thorough and realistic dataset covering a wide range of scenarios. The insufficient availability of extensive datasets in the radio frequency (RF) domain, particularly in the context of active/passive coexistence, poses a significant obstacle to progress. This limitation is particularly notable in the context of comprehending the effectiveness of conventional model-based RFI detection approaches when applied to advanced 5th-generation (5G) wireless communication signals. This study first shows the development of an experimental passive radiometer and 5G testbed system and aims to assess the efficacy of the widely employed spectral kurtosis RFI detection approach within controlled anechoic chamber experiments. Our experimental setup comprises a fully calibrated SDR-based L-band radiometer subjected to diverse 5 G wireless signals, varying in power levels, frequency resource block group allocation, and modulation techniques. Significantly, our testbed facilitates the concurrent recording of ground truth temperatures while subjecting the radiometer to 5 G signal transmission which helps to understand the overall effect in the radiometer. This distinctive configuration provides insights into the effectiveness of traditional RFI detection models, offering valuable perspectives on the associated challenges in RFI detection. 

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4. Automated RFI Flagging for HI Spectra

   Jong, Eun Ju; Hallenbeck, Gregory (January 2019, American Astronomical Society, AAS Meeting)

   Manual flagging of RFI is extremely time-consuming and error-prone. We present a machine learning algorithm which automatically identifies radio frequency interference (RFI) in HI spectra. Our algorithm uses the features of polarization asymmetry (defined as |polA - polB|/[polA + polB] ) along with the skew and standard deviation of each channel over time to evaluate the presence of RFI. The algorithm was tested on hundreds of spectra taken by the Undergraduate ALFALFA Team (UAT) as part of the APPSS survey. It outperforms humans not only in speed, but in visually identifying RFI when it is weak or mimics properties of signals. This work has been supported by NSF grants AST-1211005 and AST-1637339. 

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5. Altitude estimation of radio frequency interference sources via interferometric near-field corrections

   https://doi.org/10.1017/pasa.2024.123  

   Ducharme, Jade M; Pober, Jonathan C (January 2025, Publications of the Astronomical Society of Australia)

   Abstract Radio-frequency interference (RFI) presents a significant obstacle to current radio interferometry experiments aimed at the Epoch of Reionization. RFI contamination is often several orders of magnitude brighter than the astrophysical signals of interest, necessitating highly precise identification and flagging. Although existing RFI flagging tools have achieved some success, the pervasive nature of this contamination leads to the rejection of excessive data volumes. In this work, we present a way to estimate an RFI emitter’s altitude using near-field corrections. Being able to obtain the precise location of such an emitter could shift the strategy from merely flagging to subtracting or peeling the RFI, allowing us to preserve a higher fraction of usable data. We conduct a preliminary study using a two-minute observation from the Murchison-Widefield Array (MWA) in which an unknown object briefly crosses the field of view, reflecting RFI signals into the array. By applying near-field corrections that bring the object into focus, we are able to estimate its approximate altitude and speed to be$$11.7$$km and 792 km/h, respectively. This allows us to confidently conclude that the object in question is in fact an airplane. We further validate our technique through the analysis of two additional RFI-containing MWA observations, where we are consistently able to identify airplanes as the source of the interference. 

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