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1. Design of a Shape Optimized Printed-circuit Beamformer

   https://doi.org/10.1109/APS/URSI47566.2021.9703849  

   Szymanski, Luke; Grbic, Anthony; Gok, Gurkan (December 2021, 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI))

   This work presents the experimental realization of a printed-circuit beamformer designed using shape optimization. Shape optimization of the printed-circuit beamformer is enabled through the use of a circuit network solver that utilizes reduced-order models of printed-circuit unit cells to rapidly evaluate device responses, and the adjoint variable method to evaluate gradients. The designed beamformer is patterned on a microwave substrate and interfaces with a 3-D printed tapered aperture antenna. It produces nine orthogonal beams and has isolated input ports that are impedance matched. Experimental results for the performance of the 3-D printed antenna and the beamformer will be presented at the conference. 

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2. Evidence of Ultrafaint Radio Frequency Interference in Deep 21 cm Epoch of Reionization Power Spectra with the Murchison Wide-field Array

   https://doi.org/10.3847/1538-4357/acffbd  

   Wilensky, Michael J.; Morales, Miguel F.; Hazelton, Bryna J.; Star, Pyxie L.; Barry, Nichole; Byrne, Ruby; Jordan, C. H.; Jacobs, Daniel C.; Pober, Jonathan C.; Trott, C. M. (November 2023, The Astrophysical Journal)

   Abstract We present deep upper limits from the 2014 Murchison Widefield Array Phase I observing season, with a particular emphasis on identifying the spectral fingerprints of extremely faint radio frequency interference (RFI) contamination in the 21 cm power spectra (PS). After meticulous RFI excision involving a combination of theSSINSRFI flagger and a series of PS-based jackknife tests, our lowest upper limit on the Epoch of Reionization (EoR) 21 cm PS signal is Δ²≤ 1.61 × 10⁴mK²atk= 0.258h Mpc⁻¹at a redshift of 7.1 using 14.7 hr of data. By leveraging our understanding of how even fainter RFI is likely to contaminate the EoR PS, we are able to identify ultrafaint RFI signals in the cylindrical PS. Surprisingly this signature is most obvious in PS formed with less than 1 hr of data, but is potentially subdominant to other systematics in multiple-hour integrations. Since the total RFI budget in a PS detection is quite strict, this nontrivial integration behavior suggests a need to more realistically model coherently integrated ultrafaint RFI in PS measurements so that its potential contribution to a future detection can be diagnosed. 

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3. Simulating Spectral Kurtosis Mitigation Against Realistic RFI Signals

   Smith, E. T.'; Pisano, D. J. (September 2022, The RFI2022 Workshop at ECMWF)

   We explore the statistical radio frequency interference (RFI) mitigation technique spectral kurtosis (SK) in the context of simulated realistic RFI signals. SK is a per-channel RFI detection metric that estimates the kurtosis of a collection of M power values in a single channel to discern between human-made RFI and incoherent astronomical signals of interest. We briefly test the ability of SK to flag signals with various representative modulation types, data rates, and duty cycles, as well as accumulation lengths M and multi-scale SK bin shapes. Multi-scale SK uses a rolling window to combine information from adjacent time-frequency pixels to mitigate weaknesses in single-scale SK. High data rate RFI signals with significant sidelobe emission are harder to flag, as well as signals with a 50% effective duty cycle. Multi-scale SK using at least one extra channel can detect both the center channel and side-band interference, flagging most of the signal at the expense of larger false positive rates. 

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4. Deep Learning Based RFI Detection and Mitigation for SMAP Using Convolutional Neural Networks

   Ahmed Manavi Alam*, Ali Cafer (January 2022, RFI Workshop 2022)

   Passive Remote Sensing services are indispensable in modern society because of the applications related to climate studies and earth science. Among those, NASA’s Soil Moisture Active and Passive (SMAP) mission provides an essential climate variable such as the moisture content of the soil by using microwave radiation within protected band over 1400-1427 MHz. However, because of the increasing active wireless technologies such as Internet of Things (IoT), unmanned aerial vehicles (UAV), and 5G wireless communication, the SMAP’s passive observations are expected to experience an increasing number of Radio Frequency Interference (RFI). RFI is a well-documented issue and SMAP has a ground processing unit dedicated to tackling this issue. However, advanced techniques are needed to tackle the increasing RFI problem for passive sensing systems and to jointly coexist communication and sensing systems. In this paper, we apply a deep learning approach where a novel Convolutional Neural Network (CNN) architecture for both RFI detection and mitigation is employed. SMAP Level 1A spectrogram of antenna counts and various moments data are used as the inputs to the deep learning architecture. We simulate different types of RFI sources such as pulsed, CW or wideband anthropogenic signals. We then use artificially corrupted SMAP Level 1B antenna measurements in conjunction with RFI labels to train the learning architecture. While the learned detection network classifies input spectrograms as RFI or no-RFI cases, the mitigation network reconstructs the RFI mitigated antenna temperature images. The proposed learning framework both takes advantage of the existing SMAP data and the simulated RFI scenarios. Future remote sensing systems such as radiometers will suffer an increasing RFI problem and spectrum sharing and techniques that will allow coexistance of sensing and communication systems will be utmost importance for both parties. RFI detection and mitigation will remain a prerequisite for these radiometers and the proposed deep learning approach has the potential to provide an additional perspective to existing solutions. We will present detailed analysis on the selected deep learning architecture, obtained RFI detection accuracy levels and RFI mitigation performance. 

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5. Optimizing Sparse RFI Prediction using Deep Learning

   https://doi.org/10.1093/mnras/stz1865  

   Kerrigan, Joshua; Plante, Paul La; Kohn, Saul; Pober, Jonathan C; Aguirre, James; Abdurashidova, Zara; Alexander, Paul; Ali, Zaki S; Balfour, Yanga; Beardsley, Adam P; et al (July 2019, Monthly Notices of the Royal Astronomical Society)

   Abstract Radio Frequency Interference (RFI) is an ever-present limiting factor among radio telescopes even in the most remote observing locations. When looking to retain the maximum amount of sensitivity and reduce contamination for Epoch of Reionization studies, the identification and removal of RFI is especially important. In addition to improved RFI identification, we must also take into account computational efficiency of the RFI-Identification algorithm as radio interferometer arrays such as the Hydrogen Epoch of Reionization Array grow larger in number of receivers. To address this, we present a Deep Fully Convolutional Neural Network (DFCN) that is comprehensive in its use of interferometric data, where both amplitude and phase information are used jointly for identifying RFI. We train the network using simulated HERA visibilities containing mock RFI, yielding a known “ground truth” dataset for evaluating the accuracy of various RFI algorithms. Evaluation of the DFCN model is performed on observations from the 67 dish build-out, HERA-67, and achieves a data throughput of 1.6× 105 HERA time-ordered 1024 channeled visibilities per hour per GPU. We determine that relative to an amplitude only network including visibility phase adds important adjacent time-frequency context which increases discrimination between RFI and Non-RFI. The inclusion of phase when predicting achieves a Recall of 0.81, Precision of 0.58, and F2 score of 0.75 as applied to our HERA-67 observations. 

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