Radio Frequency Interference Sources Research Articles

Constrained Iterative Adaptive Algorithm for the Detection and Localization of RFI Sources Based on the SMAP L-Band Microwave Radiometer

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.

GNSS interference monitoring and detection based on the Swedish CORS network SWEPOS

Abstract The presence of Global Navigation Satellite System (GNSS) interference poses a significant risk to both GNSS and the infrastructures that depend on them. This article describes how an existing GNSS infrastructure, the Swedish Continuously Operating Reference Stations network SWEPOS with more than 500 stations, can be leveraged to control the quality of GNSS signals and to be able to detect interferences in GNSS and adjacent frequency bands. This study introduces a SWEPOS-based automatic system that effectively detects GNSS interference by examining the signal-to-noise ratio (SNR) of Global Positioning System, Globalnaya Navigatsionnaya Sputnikovaya Sistema, Galileo, and BeiDou frequency bands. By comparing the SNR of tracked satellites against historical data, the system detects radio frequency interference (RFI) by statistically characterizing the SNR of simultaneously tracked satellites. Furthermore, based on the SNR characteristics across satellites, the system distinguishes between RFI and non-RFI sources. The effectiveness of the detection system is demonstrated through both simulated signal interference waves and real-world interference events.

Evaluation of Application Effectiveness on Ocean Salinity Satellite RFI Detection Algorithms

In order to alleviate the impact of radio frequency interference (RFI) on the accuracy of ocean salinity satellite remote sensing, scholars have proposed various detection and labeling algorithms for RFI based on remote sensing data from the SMOS satellite. However, the signals that generate RFI are diverse, and the factors that influence remote sensing observation data are complex. Existing algorithms often target specific hypothetical conditions, lacking general applicability, which frequently leads to an important gap between the nominal performance of the literature and practical applications, posing great challenges to data labeling work. To address this problem, this study conducted a comprehensive and systematic analysis of RFI simulation based on scene modeling, algorithm modeling, and RFI energy modeling. Three typical RFI detection algorithms were selected, and the simulation scene was divided into 3 typical scenes: ocean, land, and sea–land scenes, and RFI was analyzed in terms of weak, moderate, strong, and extremely strong based on energy. Through simulation analysis and evaluation of RFI detection algorithms, lookup tables for algorithm selection, detection rate, and false-positive rate have been established for different intensities of independent RFI sources and multiple nearby RFI sources in the above scenario. These lookup tables have universal guiding significance and provide reliability assurance in complex situations.

Transient RFI environment of LOFAR-LBA at 72–75 MHz

Context. Measurement of the highly redshifted and faint 21-cm signal of neutral hydrogen from the Cosmic Dawn and Epoch of Reionisation promises to unveil a wealth of information about the astrophysical processes that governed the structure formation and evolution of the universe during the first billion years of its evolution. Aims. The AARTFAAC Cosmic Explorer (ACE) program utilises the AARTFAAC wide-field imager of LOFAR to measure the power spectrum of the intensity fluctuations of the redshifted 21-cm signal from the Cosmic Dawn at z ∼ 18 corresponding to the global 21-cm absorption feature reported by the EDGES experiment. Radio frequency interference (RFI) from various sources, such as aeroplane communication, contaminates the observed data and it is crucial to exclude the RFI-affected data in the analysis for any reliable detection. In this work, we solely focus on investigating the impact of non-ground-based transient RFI on the analysis of ACE observations. Methods. We use cross-power spectra and cross-coherence metrics to assess the correlation of RFI over time and investigate the level of impact of transient RFI on the 21-cm signal power spectrum estimation. Results. We detected moving sky-based transient RFI sources that cross the field of view within a few minutes and appear to be mainly from aeroplane communication beacons at the location of the LOFAR core in the 72−75 MHz band (a part of the EDGES absorption trough), by inspecting filtered images. We find that this transient RFI is mostly uncorrelated over time and is only expected to dominate over the thermal noise for an extremely deep integration time of 3000 h or more with a hypothetical instrument that is sky temperature dominated at 75 MHz. We find no visible correlation over different k-modes in Fourier space in the presence of noise for realistic thermal noise scenarios. Conclusions. We conclude that the sky-based transient RFI from aeroplanes, satellites and meteorites at present does not pose a significant concern for the ACE analyses at the current level of sensitivity and after integrating over the available ∼500 h of observed data. However, it is crucial to mitigate or filter such transient RFI for more sensitive experiments aiming for significantly deeper integration.

RFI detection with spiking neural networks

Abstract Detecting and mitigating radio frequency interference (RFI) is critical for enabling and maximising the scientific output of radio telescopes. The emergence of machine learning (ML) methods capable of handling large datasets has led to their application in radio astronomy, particularly in RFI detection. Spiking neural networks (SNNs), inspired by biological systems, are well suited for processing spatio-temporal data. This study introduces the first exploratory application of SNNs to an astronomical data processing task, specifically RFI detection. We adapt the nearest latent neighbours (NLNs) algorithm and auto-encoder architecture proposed by previous authors to SNN execution by direct ANN2SNN conversion, enabling simplified downstream RFI detection by sampling the naturally varying latent space from the internal spiking neurons. Our subsequent evaluation aims to determine whether SNNs are viable for future RFI detection schemes. We evaluate detection performance with the simulated HERA telescope and hand-labelled LOFAR observation dataset the original authors provided. We additionally evaluate detection performance with a new MeerKAT-inspired simulation dataset that provides a technical challenge for machine-learnt RFI detection methods. This dataset focuses on satellite-based RFI, an increasingly important class of RFI and is an additional contribution. Our SNN approach remains competitive with the original NLN algorithm and AOFlagger in AUROC, AUPRC, and F1-scores for the HERA dataset but exhibits difficulty in the LOFAR and Tabascal datasets. However, our method maintains this accuracy while completely removing the compute and memory-intense latent sampling step found in NLN. This work demonstrates the viability of SNNs as a promising avenue for ML-based RFI detection in radio telescopes by establishing a minimal performance baseline on traditional and nascent satellite-based RFI sources and is the first work to our knowledge to apply SNNs in astronomy.

A Software for RFI Analysis of Radio Environment around Radio Telescope

Radio astronomy uses radio telescopes to detect very faint emissions from celestial objects. However, human-made radio frequency interference (RFI) is currently a common problem faced by most terrestrial radio telescopes, and it is getting worse with the development of the economy and technology. Therefore, it is essential to monitor and evaluate interference during the planning, construction, and operation stages of the radio telescope and protect the quiet radio environment around the radio astronomical site. In this paper, we present a software for an RFI analysis of the radio environment around the telescope. In this software, information has been collected, including the location of the site; the technical specifications, such as aperture and the frequency range of the radio telescopes; and the terrain around the site. The software and its modules are composed of telescope, geographic, and meteorological databases, and analysis modules of terrestrial and space-based RFI. Combined with the propagation characteristics of radio waves, we can analyze and evaluate RFI on the ground and in space around the radio telescope. The feasibility of the software has been proved by the experimental implementation of the propagation properties and RFI source estimation. With this software, efficient technical support can be expected for protecting the radio environment around the telescope, as well as improving site selection for planned radio astronomical facilities.

Collaboration With Cellular Networks for RFI Cancellation at Radio Telescope

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 the Radio Telescope without any active collaboration with the interfering sources. In this work, we provide a method of signal characterization and its use in subsequent cancellation, that uses Eigenspaces derived from the telescope and the transmitter signals. This is different from conventional time-frequency domain analysis, which is limited to fixed characterizations (e.g., complex exponential in Fourier methods) that cannot adapt to the changing statistics (e.g., autocorrelation) of the RFI, typically observed in communication systems. We have presented effectiveness of this collaborative method using realworld astronomical signals and practical simulated LTE signals (downlink and uplink) as source of RFI along with propagation conditions based on preset benchmarks and standards. Through our analysis and simulation using these signals, we are able to remove 89.04% of the RFI from cellular networks, which reduces excision at the Telescope and is capable of significantly improving throughput as corrupted time-frequency bins of data become usable.

Radio frequency interference detection and mitigation in the DWD C-band weather radar network

Abstract. C-band weather radar data are commonly compromised by interference from external sources even though weather radars are the primary and therefore privileged user of this frequency band. This is also the case for the radar network of the German Meteorological Service (Deutscher Wetterdienst, DWD). Theoretically, dynamic frequency selection (DFS) by devices operating in the C band should prevent any disturbance of the primary user. In practice, this does not always work as intended under the current regulations. As it is not possible to separate a superimposed interference signal from measured weather radar data, the protection of the frequency band is of utmost importance. Currently, the only available option is to discard the compromised portions of the radar data. Therefore, the current best course of action is to shut down radio frequency interference (RFI) sources as quickly as possible. The automated RFI detection algorithm for the German C-band weather radar network, operational since July 2017, makes use of routinely measured radar moments. Built upon data gathered since 2017, an RFI classification with respect to the severity and duration of RFI sources was first implemented in 2019. An independent verification of the RFI detection algorithm was performed by using a commercially available Wi-Fi adapter, which is directly integrated into the radar receiver. Subsequently, a mitigation workflow was implemented to efficiently identify and shut down detected RFI sources by the German Federal Network Agency (Bundesnetzagentur, BNetzA). By following this workflow with great effort, the number of persistent RFI sources has been decreasing since October 2019, while a steady increase in short-lived RFI sources over the last 5 years also exists. In total, 11 889 RFI sources have been identified from July 2017 to May 2022. Most of these (94.8 %) are such short-lived sources that an unambiguous identification by the BNetzA is, in general, not feasible. However, as stated by the C-band regulations, any non-compliant transmitter compromising the operation of a weather radar has to be shut down. This is important, as even these short-lived RFI sources negatively affect meteorological product generation.

Mapping H i in the NGC 4636 Galaxy Group with FAST

This paper presents data from a 21 cm H i emission drift scan observation of a field partially covering the NGC 4636 galaxy group with the Five-hundred meter Aperture Radio Telescope (FAST). We construct a pipeline to reduce the data, and use SoFiA for source finding. When not contaminated by Radio Frequency Interference (RFI), the FAST observations are capable of detecting all of the galaxies previously detected by the Arecibo Legacy Fast ALFA (ALFALFA) survey in the same region. Comparing to ALFALFA for the detections in common, the FAST data show consistent integrated spectra when the H i disks are spatially unresolved, and capture more flux when the H i disks are resolved. The FAST data further reveal 10 new detections in the region mutually covered with ALFALFA, and 18 new detections beyond the footprint of ALFALFA. All of the new detections have the matching optical counterparts. For the member galaxies of the NGC 4636 group, the detection limit of FAST is deeper by 0.4 dex in H i mass than that of the ALFALFA data. After correcting for the incompleteness caused by RFI contamination, we show that the H i detection rate of galaxies rises steeply with radius out to the virial radius of the group, and flattens beyond that. We also examine four spatially resolved galaxy systems with potential tidal interaction features in detail. Considering that the data have been taken during the “shared-risk” period before a major source of local RFI was eliminated, the results highlight the power of FAST in detecting extragalactic H i.

RFI Mitigation for One-Bit UWB Radar Systems

Radio frequency interference (RFI) mitigation is critical to the proper operation of ultrawideband (UWB) radar systems because RFI can severely degrade the radar imaging capability and target detection performance. In this article, we address the RFI mitigation problem for one-bit UWB radar systems. A one-bit UWB system obtains its signed measurements via a low-cost and high rate sampling scheme, referred to as the continuous time binary value (CTBV) technology. This sampling strategy compares the signal to a known threshold that varies with slow-time and can be used to achieve a high sampling rate and quantization resolution with simple and affordable hardware. This article establishes a proper data model for the RFI sources and proposes a novel RFI mitigation method for the one-bit UWB radar system that uses the CTBV sampling technique. Specifically, we model the RFI sources as a sum of sinusoids with frequencies fixed during the coherent processing interval (CPI) and we exploit the sparsity of the RFI spectrum. We use an extended majorization-minimization-based 1bRELAX algorithm, referred to as 1bMMRELAX, to estimate the RFI source parameters from the signed measurements obtained by using the CTBV sampling strategy. We also devise a new fast frequency initialization method for the extended 1bMMRELAX algorithm to improve its computational efficiency. Moreover, a sparse method is introduced to recover the desired radar echoes using the estimated RFI parameters. Both simulated and experimental results are presented to demonstrate that our proposed algorithm outperforms the existing digital integration method, especially for severe RFI cases.

RFI Source Localization Based on Joint Sparse Recovery in Microwave Interferometric Radiometry

Recently, the radio frequency interference (RFI) poses a growing threat to the Microwave Interference Radiometer with Aperture Synthesis (MIRAS) led by the European Space Agency, whose scientific goal is to monitor the soil moisture and ocean salinity of the Earth. RFI localization is a critical step to mitigate the impact of RFI sources on the brightness temperature (BT) maps. In this paper, we propose an effective and robust RFI source localization approach combined with the joint sparse recovery (JSR) theory by using multi-snapshot data. The JSR method utilizes the joint sparsity property of RFI sources in the spatial domain to improve the robustness and accuracy for RFI source localization problem. First, we propose a JSR model by exploiting the joint sparsity of multi-snapshot visibility data with RFI contained from MIRAS after conducting the field of view (FOV) registration. Then, we present a greedy algorithm using the orthogonal matching pursuit on multi-snapshot data (MSOMP) to localize RFI sources. Results on synthetic data and real satellite data both show that, compared with the previous existing approaches, the proposed method has a better performance on the mitigation of the localization accuracy bias, especially for a low BT value range case and a mixed BT value range case with multiple RFI sources.

RFI Source Detection Based on Reweighted ℓ 1-Norm Minimization for Microwave Interferometric Radiometry

Radio frequency interference (RFI) seriously deteriorates the quality of the retrieval of geophysical parameters, e.g., Earth surface moisture and ocean salinity, measured in microwave interferometric radiometry (MIR). The accurate detection of RFI sources is crucial for locating these illegal sources and mitigating their impact. In this article, we propose a new method based on reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -norm minimization to detect RFI sources. First, we exploit the sparsity of RFI sources in the spatial domain and formulate the RFI detection as a problem of reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -norm minimization, by which the RFI signals can be well recovered and the background noises can be suppressed. Then, we present two algorithms, termed RL1 and NRL1, to achieve RFI source detection. The RL1 algorithm employs a fast iterative shrinkage thresholding (FIST) technique, and the NRL1 algorithm combines the FIST with a neighbor-reweighting strategy that helps to further enhance the RFI target regions. Finally, simulations and experiments using Soil Moisture and Ocean Salinity (SMOS) satellite data demonstrate the superiority of the proposed method on the RFI-signal-to-background ratio (RSBR) in recovered images and the detection performance of RFI sources, compared with the existing RFI processing methods in MIR.

Detecting GNSS Jamming and Spoofing on Android Devices

<h3>Abstract</h3> Global navigation satellite system (GNSS) location engines on Android devices provide location and navigation utility to billions of people worldwide. However, these location engines currently have very limited protection from threats to their position, navigation, and time (PNT) solutions. External sources of radio frequency interference (RFI) can render PNT information unusable. Even worse, false signals or spoofing can provide a false PNT solution to Android devices. To mitigate this, four detection methods were developed and evaluated using native location parameters within Android: Comparing the GNSS and network locations, checking the Android mock location flag, comparing the GNSS and Android system times, and observing the automatic gain control (AGC) and carrier-to-noise density (C/N₀) signal metrics. These methods provide a powerful means to significantly increase the robustness of the Android GNSS-based PNT solution and are implemented in the GNSSAlarm Android application to demonstrate real-time jamming and spoofing detection.

Detection and Localization of Terrestrial L-Band RFI With GNSS Receivers

Global navigation satellite system (GNSS) signals are critically important for a wide range of commercial, military, and science applications. Recent studies have identified threats to the performance of GNSS from both intended and unintended sources of radio frequency interference (RFI). Understanding the distribution of the sources of RFI and the nature of the signals that they are emitting is critical to determine and mitigate their effects on the measurements made by GNSS receivers. Terrestrial RFI can be substantially detrimental to the received GNSS signals, affecting the interpretation of related science measurements. NASA's Blackjack/TriG GNSS receivers are used for precise-orbit determination and radio occultation measurements, providing a data record spanning most of the earth's surface for nearly 20 years. We have developed a highly sensitive detection algorithm that uses variations in the measured signal-to-noise ratio (SNR), on the order of 10-50 s, common to all satellites to identify times and locations subject to RFI. Initial work has focused primarily on the detection of the presence of RFI and using the receiver's orbital solution to record the location of detection events. Our intermission analysis creates a unique record of global RFI with the potential for: 1) rigorous detection of the presence of interfering signals during science measurements; 2) geolocation of RFI sources; and 3) characterization of the nature of the transmitted signal to better identify intent. Preliminary analysis has shown that the presence of RFI is well correlated with regional conflicts and other geopolitical activities.

RFI Localization via Reweighted Nuclear Norm Minimization in Microwave Interferometric Radiometry

Radio frequency interference (RFI) has become an increasing and challenging problem in microwave interferometric radiometry (MIR). Accurate localization of RFI sources is helpful to provide location information for switching off unauthorized transmitters causing RFI and mitigating the impact of these RFI sources. In this article, we propose a new RFI localization method based on reweighted nuclear norm minimization (RNNM). This method exploits the low-rank property of augmented covariance matrix (ACM) collecting visibility samples in MIR and introduces a singular value weighting strategy to consider different contributions of ACM components. First, ACM is constructed from the original covariance matrix of sparse array, which increases the degree of freedom (DOF) for array processing and hence improves the angular resolution performance. Second, we present a fixed point iteration (FPI)-based RNNM Algorithm, named FRA, to achieve low-rank approximation of ACM involving contribution degrees of ACM components. In this way, the ACM components corresponding to RFI signals are retained well and ones corresponding to background noises are suppressed. Third, we use a subspace-based direction-of-arrival (DOA) estimation approach, i.e., MUSIC algorithm, on the weighted completed ACM (WCACM) (obtained by FRA in the second stage) to locate the potential RFI sources. Retrieved results using synthetic data and real soil moisture and ocean salinity (SMOS) satellite data demonstrate that the proposed RNNM-based method not only has the superiority on improved detection performance, especially for identifying weak sources, but also shows better or competitive localization accuracy and angular resolution, compared with the existing commonly used RFI localization methods in MIR.

BSF: Block Subspace Filter for Removing Narrowband and Wideband Radio Interference Artifacts in Single-Look Complex SAR Images

Radio signals emitted by various sources, such as ground radars and broadcast/communication devices, can unintentionally cause radio frequency interference (RFI) to spaceborne synthetic aperture radar (SAR), degrading SAR image qualities to various degrees. Most existing methods tackle this problem by applying specially designed preprocessing steps to RFI-polluted level-0 SAR data before SAR focusing. However, such preprocessing is not widely used in spaceborne SAR, as there exist radiometric artifacts due to various RFI sources in the level-1 single-look complex (SLC) image products in many spaceborne SAR data, e.g., Sentinel-1 open data archives. To address this problem, in this article, we first propose a generic subspace model for characterizing a variety of RFI types, which reveals a low-dimensional structure of RFI subspace. Based on the proposed model, we next design a block subspace filter (BSF) for removing RFI artifacts in SLC SAR images directly. Experiments with ERS-2, ENVISAT/ASAR, Sentinel-1, and Gaofen-3 data are presented, and quantitative assessments based on numerical simulations are provided, which demonstrates the promising performance and application potentials of the proposed method. BSF is simple yet efficient and does not require performing preprocessing on level-0 raw data, which is helpful for users to obtain clean SAR images. MATLAB/Octave code implementation of BSF is available at <uri>https://github.com/huizhangyang/BSF</uri>.

Observation and Mitigation of Mutual RFI Between SAR Satellites: A Case Study Between Chinese GaoFen-3 and European Sentinel-1A

In synthetic aperture radar (SAR) data processing, radio frequency interference (RFI) has been recognized as a challenging issue, which can significantly degrade the image quality, including amplitude, phase, and geometry, etc. Most of the RFI sources are direct waves transmitted from the ground. Recently, a new form of RFI, namely the mutual RFI (MRFI), which is a kind of scatter-wave RFI originating from the nearby ground area simultaneously illuminated by different SAR satellites, has been reported. In this paper, the signatures of MRFI are characterized, and an example case study between Chinese GaoFen-3 (GF-3) and European Sentinel-1A (S-1A) is analyzed. In order to remove the artifacts caused by MRFI on SAR images, a novel RFI detector based on spectrum energy cancellation (SEC), which has the capability of detecting MRFI, is developed. Two methods, i.e., the traditional notch filtering method and an improved eigen-subspace projection (ESP) method proposed in this paper, are employed to mitigate MRFI. The former method has robust mitigation performance with a fast processing speed, whereas the latter has an improved mitigation accuracy as well as lower side-lobes for strong scatterers. The methods are designed for cases in which the MRFI has a different radar centre frequency than the useful signal, and some of the data is free from MRFI. The conditions required for the proper use of the approach are discussed. In contrast to the conventional mitigating methods performed on the raw data domain, the proposed approach begins with the focused single look complex (SLC) SAR data. The effectiveness of the proposed approach is demonstrated on several GF-3 SLC SAR images, which were contaminated by MRFI from the S-1A.

Improved CLEAN Algorithm for RFI Mitigation of Aperture Synthesis Radiometer Images

For synthetic aperture radiometer, radio frequency interference (RFI) seriously contaminates the observed data and affects the quality of the brightness temperature (BT) image. CLEAN algorithm is usually used to mitigate the Gibbs effect generated by RFI. However, the closely distributed RFI sources cannot be well mitigated by the original CLEAN algorithm. This letter proposes an improved adaptive CLEAN algorithm to enhance the performance of RFI mitigation, especially the closely distributed RFI sources. The loop factor is forced to synchronize with residual BT of RFI for every iteration to avoid over mitigation. Over mitigation can result in a BT vacancy area or an extra BT point generated at the position of RFI. Soil Moisture and Ocean Salinity (SMOS) L1B real data are applied to verify the effectiveness of the improved CLEAN algorithm. The simulation shows that the improved CLEAN algorithm can be competent in eliminating isolated and closely distributed RFIs better than the classic CLEAN algorithm, and the over mitigation problem is solved by the proposed method.

Joint RFI mitigation and radar echo recovery for one-bit UWB radar

Radio frequency interference (RFI) mitigation and radar echo recovery are critically important for the proper functioning of ultra-wideband (UWB) radar systems using one-bit sampling techniques. We recently introduced a technique for one-bit UWB radar, which first uses a majorization-minimization method for RFI parameter estimation followed by a sparse method for radar echo recovery. However, this technique suffers from high computational complexity due to the need to estimate the parameters of each RFI source separately and iteratively. In this paper, we present a computationally efficient joint RFI mitigation and radar echo recovery framework to greatly reduce the computational cost. Specifically, we exploit the sparsity of RFI in the fast-frequency domain and the sparsity of radar echoes in the fast-time domain to design a one-bit weighted SPICE (SParse Iterative Covariance-based Estimation) based framework for the joint RFI mitigation and radar echo recovery of one-bit UWB radar. Both simulated and experimental results are presented to show that the proposed one-bit weighted SPICE framework can not only reduce the computational cost but also outperform the existing approach for decoupled RFI mitigation and radar echo recovery of one-bit UWB radar.

Multi-dimensional radio frequency interference framework for characterizing radio astronomy observatories

Radio frequency interference (RFI) has historically plagued radio astronomy, worsening with the rapid spread of electronics and increasing telescope sensitivity. We present a multi-dimensional probabilistic framework for characterizing the RFI environment around a radio astronomy site that uses data from the telescope observations themselves. We apply the framework to about 1500 h of commissioning data (∼200 TB) from the MeerKAT radio telescope; producing a six-dimensional array that yields both average RFI occupancy as well as distributions around the mean as a function of key variables (time of day, frequency, baseline, azimuth, and elevation). This allows for automated alerting for significant new sources of RFI and monitoring of RFI trends. Our results provide the first detailed view of the MeerKAT RFI environment at high sensitivity. As expected, in the MeerKAT L-band, we find that the major RFI contributors are from the global positioning system satellites, flight distance measurement equipment, and the global system for mobile communications. Beyond characterizing RFI environments, our approach allows observers access to the prior probability of RFI in any combination of tracked variables, allowing for more efficient observation planning and data flagging.