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Radio Frequency Interference (RFI) significantly degrades the quality of spaceborne Synthetic Aperture Radar (SAR) images, and RFI source localization is a crucial component of SAR interference mitigation. Single-station, single-channel SAR, referred to as single-channel SAR, is the most common operational mode of spaceborne SAR. However, studies on RFI source localization for this system are limited, and the localization accuracy remains low. This paper presents a method for locating the ground-based RFI source using spaceborne single-channel SAR echo data. First, matched filtering is employed to estimate the range and azimuth times of the RFI pulse-by-pulse in the SAR echo domain. A non-convex localization model using Pulse Range Difference of Arrival (PRDOA) is established based on the SAR observation geometry. Then, by applying Weighted Least Squares and Semidefinite Relaxation, the localization model is transformed into a convex optimization problem, allowing for the solution of its global optimal solution to achieve RFI source localization. Furthermore, the error analysis on the PRDOA localization model is conducted and the Cramér–Rao Lower Bound is derived. Based on the simulation platform and the SAR level-0 raw data of Gaofen-3, we conduct several verification experiments, with the Pulse Time of Arrival localization selected for comparison. The results demonstrate that the proposed method achieves localization accuracy with a hundred-meter error in azimuth and a kilometer-level total error, with the total localization errors reduced to approximately 1/4 to 1/3 of those of the Pulse Time of Arrival method. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. Meanwhile, current deep learning-based algorithms are often unable to locate the range of RFI coverage in the azimuth direction. Therefore, a novel RFI processing framework from quick-looks to single-look complex (SLC) data and then to raw echo is proposed. We take the data of Sentinel-1 terrain observation with progressive scan (TOPS) mode as an example. By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. Then, through the analysis of the TOPSAR imaging principle, the position of the RFI in the SLC image is preliminarily confirmed. The possible distribution of the RFI in the corresponding raw echo is further inferred, which is one of the first attempts to use spaceborne SAR data to elucidate the RFI location mapping relationship between image data and raw echo. Compared with directly detecting all of the SLC data, the time for the proposed framework to determine the RFI distribution in the SLC data can be shortened by 53.526%. All the research in this paper is conducted on Sentinel-1 real data, which verify the feasibility and effectiveness of the proposed framework for radio frequency signals monitoring in advanced spaceborne SAR systems. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) analysis is crucial for ensuring the proper functioning of a radio telescope and the quality of astronomical observations, as human-generated interference can compromise scientific data collection. The aim of this study is to present the results of an RFI measurement campaign in the frequency range of 4–5.8 GHz, a portion of the well-known C-band, for the Sardinia Radio Telescope (SRT), conducted in October–November 2023. In fact, this Italian telescope, managed by the Astronomical Observatory of Cagliari (OAC), a branch of the Italian National Institute for Astrophysics (INAF), was recently equipped with a new C-band receiver that operates from 4.2 GHz to 5.6 GHz. The measurements were carried out at three strategically chosen locations around the telescope using the INAF mobile laboratory, providing comprehensive coverage of all possible antenna pointing directions. The results revealed several sources of RFI, including emissions from radar, terrestrial and satellite communications, and wireless transmissions. Characterizing these sources and assessing their frequency band occupation are essential for understanding the impact of RFI on scientific observations. This work provides a significant contribution to astronomers who will use the SRT for scientific observations, offering a suggestion for the development of mitigation strategies and safeguarding the radio astronomical environment for future observational campaigns. [ABSTRACT FROM AUTHOR]

For synthetic aperture microwave radiometers, the problem of Radio Frequency Interference (RFI) is becoming more and more serious, which affects both the scientific retrieval of remote sensing data and the imaging quality of brightness temperature (BT) images. In the visibility data domain, the array factor synthesis algorithm is commonly employed to mitigate RFI sources and their Gibbs trailing. In the BT image domain, the CLEAN algorithm is typical applied to mitigate RFI sources and their Gibbs trailing. However, the array factor synthesis algorithm can result in anomalous BT points near the "zero trap" region, and the CLEAN algorithm will miss some BT points below a certain threshold. In this paper, a Bi-domain combined mitigation algorithm is proposed to mitigate RFI sources and their Gibbs trailing. Following initial mitigation in the visibility data domain, dual thresholds are applied to normalize anomalous BT points near the "zero trap" region, thereby enhancing imaging quality. The effectiveness of the Bi-domain combined mitigation algorithm is verified by using both measured data from SMOS L1A and simulated data. The experimental results demonstrate that the Bi-domain combined mitigation algorithm is superior to the array factor synthesis algorithm and the CLEAN algorithm in mitigating RFI sources and their Gibbs trailing. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

The accuracy of geophysical retrievals from radiometers relies on calibration quality, encompassing both absolute radiometric accuracy and spectral consistency. Radiometers have employed various calibration techniques, including external targets, vicarious sources, and internal calibrators like noise diodes or matched reference loads. Calibration techniques face challenges like frequency dependence, instrumental effects, environmental influences, drift, aging, and radio frequency interference. Recent hardware advancements enable radiometers to collect raw samples containing both temporal and spectral information. Leveraging advanced modeling techniques like deep learning (DL) enables detecting subtle correlations, non-linear dependencies, and higher-order interactions within the data extracting valuable information that may have been challenging with conventional methods. This study utilizes NASA's Soil Moisture Active Passive (SMAP) satellite's level 1A and level 1B data products to develop a DL-based radiometer calibrator to estimate antenna temperature. Spectrograms of second raw moments equivalent to power carrying the 2-D spectral features serve as primary input in a supervised convolutional neural network-based architecture. DL-based calibrator has demonstrated high correlation and low root mean square error when incorporating spectral information from both reference and noise diodes and when not considering this information. Findings suggest that the ancillary features such as internal thermistor temperature and loss elements exhibit sufficient accuracy in estimating antenna temperature to compensate for variations in receiver noise temperature and short-term gain fluctuations in the absence of the reference load and noise diode power. The proposed calibration technique with reduced reference information might enable radiometers for a higher number of antenna scene observations within a footprint.

The existence of radio frequency interference (RFI) has a great influence on the measurement of synthetic aperture interferometric radiometer (SAIR). The strong RFI signal energy picked up by the sidelobes of synthetic beam (or array factor) of SAIR easily results in false positives, deteriorating the RFI detection performance, especially when considering the representative scenes where RFIs have a large dynamic range of intensity. In this article, we propose a novel RFI source detection method by exploiting the array factor property (AFP) of SAIR. The AFP-based RFI detection method mainly consists of three steps. First, RFI sources are recovered from visibility function samples through sparse reconstruction (SR). In the recovered RFI map, the background noise is reduced and the potential RFI target regions could be extracted well. Second, the AFP of SAIR is analyzed concerning distribution characteristics of main beam and sidelobes. Based on the AFP analysis, we present a new spatial weight indicator (SWI) describing the probability of one potential RFI being a false positive. Then, an SWI-based probability map (SPM) is generated to discriminate true sources with the false positive sources. Third, the SPM and the SR-based map are combined to reconstruct the RFI image, where false positives are filtered out while true RFI sources are retained. The experiments using synthetic data and real SMOS satellite data demonstrate the effectiveness and superiority of the proposed method in RFI detection tasks for SAIR.

The existence of radio frequency interference (RFI) significantly affects the quality of synthetic aperture radar (SAR) images. Locating RFI source serves as a component of electromagnetic situational awareness, which provides preliminary information for intelligent anti-interference. In this article, an RFI source localization method was proposed. Specifically, the SAR raw data both in ascending and descending passes were integrated to obtain the localization information of RFI. The localization index (LI) was first proposed to locate the RFI source after RFI detection and extraction. Subsequently, the location of the RFI source is determined by referring to the LI and the orbit inclination of the satellite-borne SAR based on radiation pattern weighing (RPW) in azimuth. Finally, the localization accuracy of the proposed method in both side-looking and squint situations is verified through simulation experiments. Based on the SAR level-0 raw data of sentinel-1 in Dubai and Jinan, the localization results show that the RFI source are near the city center or the airport. It provides a high-precision and convenient RFI source localization solution for anti-interference as well as monitoring RFI, and theoretical analyses indicate significant improvements in localization accuracy compared to the traditional X-mark method.

This paper investigates the challenges and solutions related to Radio Frequency Interference (RFI) in mobile devices equipped with high-resolution cameras, focusing on the Mobile Industry Processor Interface (MIPI) C-PHY interface. The compact design of modern mobile devices makes them susceptible to RFI, which can degrade signal quality and cause visual anomalies. Through simulation-based analysis, we explore the effectiveness of Common Mode Filters (CMFs) in mitigating RFI, highlighting the significance of CMF placement near the Board-to-Board (BTB) connector for enhanced RF immunity and signal integrity. Additionally, we examine receiver compensation techniques to further protect C-PHY signals against RFI. These techniques address the issue of signal imbalances, which can lead to differential-mode interference. The study proposes fine-tuning signal path delays at the CPU receiver end as a strategy to minimize noise, demonstrating improvements in eye diagram metrics. Our findings offer valuable insights into optimizing the placement of CMFs and implementing receiver compensation techniques, contributing to the enhancement of signal integrity and noise reduction in high-speed digital interfaces, thereby ensuring the reliability and performance of camera systems in mobile devices within the RF/EMC/SIPI domain.

The passive and active L-band system (PALS) is an airborne microwave instrument suite that combines a separate radar and radiometer airborne instrument with a common antenna. PALS has been used for salinity, wind speed, and soil moisture (SM) measurements, which is critical for calibration and validation of the SM active passive satellite brightness temperature measurements. The combined 1.413 GHz radiometer and 1.26 GHz radar measurements can provide high-accuracy SM measurements over vegetated areas. The new upgraded version of the PALS instrument employs a novel single-chain receiver topology and combines the radar and radiometer instruments. The reduced-noise single-chain receiver PALS instrument was deployed on a DC-3 aircraft for SM measurement in the summer of 2019.

As next-generation communication services and satellite systems expand across diverse frequency bands, there is a pressing need to use the spectrum as efficiently as possible. A possible strategy to achieve this is to facilitate the coexistence of different radio services, for example between telecommunication and microwave remote sensing systems. However, this approach could increase the risk of radio frequency interference (RFI) affecting passive microwave sensors, which are crucial for environmental and geoscience applications. Therefore, the availability of a testbed along with a dataset encompassing a diverse array of scenarios under a controlled environment is very important in order to study coexistence scenarios and reduce the risk of interference associated with them. This study presents a physical environmentally controlled testbed including a passive fully calibrated L-band radiometer with a digital back-end capable of collecting raw in-phase/quadrature (IQ) samples and an active fifth-generation (5G) wireless communication system with the capability of transmitting waveforms with advanced modulations. Various RFI situations such as in-band, transition band, and out-of-band transmission effects are quantified in terms of their effect on the measured brightness temperature. A detailed procedure and publicly accessible dataset are provided to help test the impact of wireless communication on passive sensing, enabling the scientific community to facilitate coexistence research and quantify interference effects on radiometers. This dataset includes publicly available raw radiometer and 5G communication samples along with preprocessed time-frequency representations and true brightness temperature data.

Due to the complex electromagnetic environment and the increased demand for frequency occupation, radio frequency interference (RFI) is becoming a significant issue for the bistatic synthetic aperture radar (BiSAR), especially in the synchronization links. This article proposes a novel rank prior matrix recovery (RPMR) method based on the robust principal component analysis (RPCA) to suppress the interferences in phase synchronization of BiSAR, by which the high-accuracy synchronization phase can be acquired. First, a signal model considering the RFI in phase synchronization is proposed, proving that the synchronization signal matrix and the RFI signal matrix have the low-rank and sparsity properties in the range frequency domain, respectively. Thus, the constraint conditions of RPCA can be satisfied. Then, the RPMR method is proposed to acquire the low-rank matrix representing the synchronization signal and the sparsity matrix representing the RFI signal. In RPMR, the partial singular value decomposition utilizing the rank prior is used to replace the traditional total singular value decomposition, thus reducing the computational cost. Finally, the high-accuracy synchronization phase can be extracted from the synchronization signal by the pulse compression. Simulation and experimental results using the real measured synchronization data of LuTan-1 validate the superiority of the proposed method.

Space-borne synthetic aperture radar (SAR), as an all-weather observation sensor, is an important means in modern information electronic warfare. Since SAR is a broadband active radar system, radio frequency interference (RFI) in the same frequency band will affect the normal observation of the SAR system. Untangling the above problem, this research explores a quick and accurate method for detecting and segmenting RFI-contaminated images. The purpose of the current method is to quickly detect the existence of RFI and to locate it in massive SAR data. Based on deep learning, the method shown in this paper realizes the existence of RFI by determining the presence or absence of interference in the image domain and then performs pixel-level image segmentation on Sentinel-1 RFI-affected quick-look images to locate RFI. Considering the need to quickly detect RFI in massive SAR data, an improved network based on MobileNet is proposed, which replaces some inverted residual blocks in the network with ghost blocks, reducing the number of network parameters and the inference time to 6.1 ms per image. Further, this paper also proposes an improved network called the Smart Interference Segmentation Network (SISNet), which is based on U2Net and replaces the convolution of the VGG blocks in U2Net with a residual convolution and introduces attention mechanisms and a modified RFB module to improve the segmentation mIoU to 87.46% on average. Experiment results and statistical analysis based on the MID dataset and PAIS dataset show that the proposed methods can achieve quicker detection than other CNNs while ensuring a certain accuracy and can significantly improve segmentation performance under the same conditions compared to the original U2Net and other semantic segmentation networks. [ABSTRACT FROM AUTHOR]

Low-frequency aperture arrays represent sensitive instruments to detect signals from radio astronomic sources situated in the universe. In Italy, the Sardinia Aperture Array Demonstrator (SAAD) consists of an ongoing project of the Italian National Institute for Astrophysics (INAF) aimed to install an aperture array constituted of 128 dual-polarized Vivaldi antennas at the Sardinia Radio Telescope (SRT) site. The originally envisaged 128 elements of SAAD were re-scoped to the 16 elements of its precursor named SADino, with the aim to quickly test the system with a digital beam-former based on the Italian Tile Processing Module (iTPM) digital back-end. A preliminary measurements campaign of radio frequency interference (RFI) was performed to survey the less contaminated spectral region. The results of these measurements permitted the establishment of the technical requirements for receiving a chain for the SADino telescope. In this paper, the design, implementation, and characterization of this signal acquisition chain are proposed. The operative frequency window of SAAD and its precursor, SADino, sweeps from 260 MHz to 420 MHz, which appears very attractive for radio astronomy applications and radar observation in space and surveillance awareness (SSA) activities. [ABSTRACT FROM AUTHOR]

The radio frequency interference (RFI) and deceptive jamming (DJ), as two major external threats to synthetic aperture radar (SAR) systems, can greatly reduce the readability and veracity of the obtained SAR images. Current interference suppression methods have no capability to suppress both of them. In this article, a subpulse coding (SPC)-based joint RFI and DJ suppression method for single-channel SAR systems is proposed. By making full use of the elaborate coding scheme and subpulse transmitting mode, SPC can effectively suppress RFIs in the Doppler domain. On the other hand, after the decoding process, by utilizing the subpulse digital beamforming (DBF) technology with the well-designed DBF weight vectors, DJs can also be suppressed greatly. Numerical experiments verify the effectiveness of the proposed method.

Radio frequency interference (RFI) is a serious threat to the accurate estimation of critical geophysical parameters via passive microwave remote sensing and the presence of RFI in microwave radiometer measurements is increasing over time. On the other hand, the nature and the occurrence of RFI captured by radiometers are usually unknown making their detection and mitigation difficult. To overcome this challenge, this article presents a novel RFI detection algorithm that relies only on the information extracted from the RFI-free radiometer measurements which can be collected over oceans and rural areas with limited human activity, i.e., a one-class algorithm, to be implemented in future remote sensing radiometers. The algorithm transforms raw time-series radiometer measurements into a heterogeneous feature-based representation. Then, a feature selection algorithm identifies the most discriminant features to detect interference based on the probabilities of misdetections and false alarms. Finally, the optimal decision boundaries that discriminate the RFI-contaminated radiometer measurements from the RFI-free ones are computed via support vector machines (SVM) using only the RFI-free radiometer measurements. Regardless of the characteristics of RFI contamination, the algorithm, therefore, outputs a generalized decision boundary for RFI-free measurements. A performance evaluation of the proposed algorithm against the traditional RFI detection algorithms has been performed using simulated radiometer data, and the results have shown that the novel algorithm, unlike the traditional methods, can successfully detect RFI, even when the interference-to-noise ratio (INR) of the radiometer measurements is as low as $-18$ dB.

In the process of radio frequency interference (RFI) suppression for synthetic aperture radar (SAR), when frequencies of RFIs are not located on discrete grids of the frequency spectrum, i.e., RFIs are off-grid, energies of RFIs will spread out, degrading suppression performances of lots of existing methods. To this end, aiming at the off-grid RFI suppression problem, in this article, a mismatch reconstruction and dictionary rotation-based RFI suppression method is proposed. First, using the defined support set, an optimization model is constructed to reconstruct mismatches of off-grid RFIs. Then, these mismatch variables are utilized to rotate the original Fourier transform-based dictionary so that frequencies of RFIs can be well matched. The energies of RFIs can be refocused under the representation of rotated dictionaries. At this time, we construct and solve another optimization problem to subtract RFIs and obtain the cleaned SAR signals. The real-world SAR data and simulated RFI data-based experiments demonstrate the effectiveness of the proposed method.

Synthetic Aperture Interferometric Radiometer (SAIR) as one of the most advanced instruments for Sea Surface Salinity (SSS) observation, has been in service on SMOS mission for years and is planned on the Chinese Ocean Salinity Satellite in the near future. However, a lot of Radio Frequency Interference (RFI) emissions are found in SMOS views, which contaminate the brightness temperature measurements of the SAIR instrument, and further impede the retrieval of SSS fields. Concerning SAIR’s operating mode, this study proposes an RFI mitigation method comprising two algorithms for co- and cross-polarization, respectively. First, RFI signatures are identified based on a series of thresholds defined by radiation theory, and then mitigated through a simple machine learning technique of Support Vector Regression (SVR), leveraging either SAIR’s multi-angle measurements or sea surface roughness descriptors, depending on the specific polarization mode. Finally, the outputs of all polarizations are merged and written back to the Level 1C brightness temperature product as the final result. Using the proposed method, the notable outliers arose from RFI contamination are attenuated, and the variation of standard deviations over nearby snapshots is smoothed, as expected on a homogeneous ocean. Furthermore, with the official L2OS software implementing the SSS retrieval procedure from the rewritten Level 1C brightness temperatures, the data re-gain of SSS fields is achieved in some places that are not attainable for the current SMOS Level 2 SSS products, with a reasonable error compared to WOA2009 SSS, confirming the validity of the proposed method. Hopefully, this work could provide a practical solution to current and future SAIR observing predicaments.

Radio frequency interference (RFI) represents all unwanted signals detected by radio receivers of a telescope. Unfortunately, the presence of RFI is significantly increasing with the technological development of wireless systems around the world. For this reason, RFI measurement campaigns are periodically necessary to map the RFI scenario around a telescope. The Sardinia Radio Telescope (SRT) is an Italian instrument that was designed to operate in a wide frequency band between 300 MHz and 116 GHz. One of the receivers of the telescope is a coaxial cryogenic receiver that covered a portion of the P and L bands (i.e., 305–410 MHz and 1300–1800 MHz) in its original version. Although the receiver was used for years to observe bright sources with sufficient results, its sub-bands can be redesigned considering the most recently evolved RFI scenario. In this paper, the results of a RFI measurement campaign are reported and discussed. On the basis of these results, the new sub-bands of the L-P receiver, together with the design of the new microwave filter selector block of the SRT receiver, are presented. In this way, SRT will cover up to 120 MHz and 460 MHz of −3 dB bandwidth at the P-band (290–410 MHz) and L-band (1320–1780 MHz), respectively. The bands of these filters are selected to reject the main RFI with high levels of amplitude and optimize the estimated antenna temperature and sensitivity of the receiver during the research activities, such as pulsar observations, very long baseline interferometer applications and spectroscopy science. [ABSTRACT FROM AUTHOR]

This letter pays attention to the mitigation of the radio frequency interference (RFI) sources located at coastal regions for synthetic aperture interferometric radiometer (SAIR). To remove an RFI at the visibility level, it will be necessary to estimate the brightness temperature (BT) of the point source, which is currently done by computing the median of the surrounded pixels to get the background BT and subtracting it from the original contaminated BT. However, this strategy is based on the assumption that the background BT distribution around the RFI is flat, and this is not reasonable for those RFIs from coastal cities and could result in estimation deviation. A robust RFI mitigation method is proposed without introducing complex computing. Instead of focusing only on the local area, the basic idea of our method is to step into the wide area and minimize the overall fluctuation of the pixels affected by the RFI in the BT image. The method tests based on SMOS visibility samples validate the correctness of the developed method. [ABSTRACT FROM AUTHOR]

In this paper, we compare the performances of radio frequency interference (RFI) suppression between radio frequency interference mitigation (RFIM) and frequency-domain adaptive clutter suppression (FACS) algorithms that apply to ionograms for foF2 scaling through true height analysis. We find that the use of RFIM and FACS can both effectively suppress RFIs in ionograms to improve the signal-to-clutter (SCR) of the original ionogram. Overall, the RFI clutter suppressed by FACS is about 5–7 dB better than that by RFIM. In addition, the SCR improvement made by FACS with neighboring range cells in a range of 10–40 is also better than that made by RFIM, which is about 5–10 dB for FACS and 4–8 dB for RFIM, respectively. However, the signal-to-noise ratios (SNRs) of the ionospheric echoes of the ionograms with FACS are about 3–5 dB (2–3 dB) smaller than those of RFIM during daytime (nighttime). Intercomparisons reveal that the correlations of the scaled foF2 values between the original and RFIM ionograms are slightly higher than those between the original and FACS ionograms, while the root-mean-square-error (RMSE) and mean bias of the former are slightly larger than the latter. These results indicate that the use of RFIM and FACS algorithms to suppress RFIs in ionograms can conditionally improve the quality of scaled foF2 using Ke et al.'s autoscaling algorithm. Statistical results reveal that the use of FACS algorithms can result in about a 5–12% improvement in uncertainty and a 9–80% improvement in accuracy, provided that 5–20 neighboring range cells are selected. As for the RFIM algorithm with a threshold of 2–2.5 times standard deviation, the improvements in uncertainty and accuracy are, respectively, about 0.9–10% and 11–90%. [ABSTRACT FROM AUTHOR]

In the PCB process, overcoming common-mode noise radiation is critical. In past years, most studies have focused on a common-mode noise filter (CMNF) that can solve electromagnetic interference in high-speed digital systems by blocking and absorbing common-mode noise radiation. Unfortunately, connecting with any reflective common-mode noise filter (R-CMNF) and reducing the area of an absorptive common-mode noise filter (A-CMNF) are the most troublesome tasks in the PCB process. A novel equivalent circuit is proposed in this research to minimize the complexity of the design and improve accuracy. Detailed analyses of this proposed approach are entirely depicted in this article. The experiment result shows that 9% of fractional bandwidth centered at 2.25 Hz can achieve at least 90% absorption efficiency. With our proposed method, the area of A-CMNF is smaller than in state-of-the-art research. [ABSTRACT FROM AUTHOR]

This paper is aimed at evaluating the performance of a Garmin GPSMAP 66sr multi-GNSS receiver via GNSS simulation. The study focuses on two important parameters in evaluating GNSS performance, namely performance under varying GNSS signal power levels and radio frequency interference (RFI). This study is conducted for the GPS L1 coarse acquisition (C/A) and Galileo E1 open service (OS) signals, for two conditions: 1) GPS only with six satellites (assuming an obstruction scenario with limited view of satellites); 2) GPS with six satellites and Galileo with six satellites. It is observed that at lower GNSS signal power levels and higher interference signal power levels, the higher number of GNSS signals observed for the GPS + Galileo mode allows it to have lower probable errors as compared to the GPS only mode. This highlights the advantage of multi- GNSS in degraded environments, such as dense urban and forest areas. [ABSTRACT FROM AUTHOR]

Passive remote sensing is a crucial technology for climate studies and Earth science. National Aeronautics and Space Administration's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400–1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1 A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.

The issue of Radio Frequency Interference (RFI) is a widespread problem in most microwave Earth observation missions, and passive instruments are particularly sensitive to RFI. This is the case for SMOS, Soil Moisture and Ocean Salinity, a satellite of the European Space Agency, which operates in the 1400–1427 MHz band, where all emissions are prohibited. Notwithstanding this regulatory framework, SMOS has been affected by RFI all around the world since the beginning of operations in 2010. In the first years of operations, manual detection processes and reporting of RFI to National Regulatory Authorities were in place in order to mitigate the detected sources. After 12 years, a tool called ERMIT (ESA RFI Monitoring and Information Tool) has been developed at ESAC (European Space Astronomy Center). This tool helps the SMOS RFI team in its spectrum monitoring tasks (e.g., RFI monitoring, logging, and reporting) thus allowing it to counteract RFI pollution more efficiently, providing external users with detailed and user-friendly information on the L-band RFI observed by SMOS. The ERMIT tool is now publicly available. This document aims at describing the needs that lead to the development of ERMIT and at presenting the information made available by it. [ABSTRACT FROM AUTHOR]

To mitigate Radio Frequency Interference (RFI) for polarimetric Doppler weather radars, this paper proposes to use spectral polarimetric filters. Polarimetric weather radars can be divided into two basic categories: Simultaneously Transmitting and Simultaneously Receiving (STSR) and Alternately Transmitting and Simultaneously Receiving (ATSR). First, the real RFI measurements from an operational C-band STSR weather radar help characterize the temporal, spectral and polarimetric features of RFI. Then, RFI is simulated in an X-band ATSR radar to quantify the performances of spectral polarimetric filters. Overall, spectral polarimetric filters can keep the precipitation and remove RFI in an ATSR radar. Finally, the data division method is put forward for STSR radars by mimicking the ATSR measurements. Good performance in RFI mitigation is also verified by using the same spectral polarimetric filters.

Synthetic aperture radar (SAR), as a wideband radar system, is easily contaminated by radio frequency interference (RFI), which affects the imaging quality of SAR. The subband spectral cancellation (SSC) method and its modifications utilize the SAR single-look complex (SLC) image to realize RFI extraction and mitigation by subtracting between sub-images, which are robust and efficient for engineering applications. In the past, the traditional SSC was often applied to narrowband interference (NBI) mitigation. However, when it was used for wideband interference (WBI) mitigation, it would cause the mitigated image to lose much of its useful information. In contrast, this paper proposes an improved SSC method based on successive cancellation and data accumulation (SSC-SCDA) for WBI mitigation. First, the fast Fourier transform (FFT) is used to characterize the SAR SLC data in the frequency domain, and the average range spectrum algorithm is used to detect whether there are interference components in the SAR SLC data. Then, according to the carrier frequency and bandwidth of the RFI in the frequency domain, the subbands are divided, and a cancellation strategy is formulated. Finally, based on the successive cancellation and data accumulation technology, WBIs can be removed by using only a small percentage of the clean subbands. Based on the simulated experiments, the interference mitigation performance of the proposed method is analyzed when the interference-to-signal bandwidth ratio (ISBR) varies from 20% to 80% under different signal-to-interference-to-noise ratios (SINR). The experimental results based on WBI-contaminated European Space Agency (ESA) Sentinel-1A SAR SLC data demonstrate the effectiveness of the proposed method in WBI mitigation. [ABSTRACT FROM AUTHOR]

Currently, the spaceborne synthetic aperture radar (SAR) system transmits a great deal of data to the ground processing station and generates massive images daily, only a tiny fraction of which contains radio frequency interference (RFI). However, most of the existing RFI detection methods are based on the prior conditions in which the known image contains interference. In fact, it is difficult to learn whether SAR images contain RFIs without prescreening, so it is of great significance to the rapid and real-time screening and detection of RFI in SAR images. This article proposes a method to screen and detect RFI from massive SAR images simultaneously. 1) We construct an approximate RFI-free background image by using the preprocessed time-series SAR images acquired in the past. 2) We generate difference images based on the image change detection method and analyze them by using an adaptive threshold, then calculate the entropy of all difference images to complete the preliminary screening of the RFI-containing). According to the preliminary results, we remove the RFI-containing parts; after reconstructing the background, we repeat step 2 with the images to be detected and obtain the final screening and detection results. Massive experimental results based on Sentinel-1 images validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) is a significant threat to accurate microwave remote sensing. The RFI signals manifest themselves in unpredictable locations and patterns in the image, which will cause measurement distortion and image degradation or even lead to wrong retrievals of the geophysical parameters. Accurate detection of RFI artifacts is a prerequisite step to preserve the overall quality of remote sensing quality. In this article, a semantic cognitive enhancement network for RFI signature detection is proposed. It employs an encoder–decoder architecture, which incorporates the atrous spatial pyramid pooling, depthwise convolution, and self-attentional mechanism. Rather than detecting the existence of RFI artifacts for an entire image, the proposed scheme can realize RFI recognition in a pixelwise manner without setting predefined thresholds. Extensive experimental results on diverse scenarios in Sentinel-1 images with various RFI types are provided, which demonstrates robust detection performance for both strong and weak interference without requiring a large number of training samples. [ABSTRACT FROM AUTHOR]

This article focuses on realizing wideband compartment shielding in the shielding can packages of miniaturized high-speed chip modules and circuits. Because the conventional diaphragms with a low profile are difficult to cope with the high-order mode interference and evanescent wave interference, a new “meta-diaphragm,” which is based on the single-negative (SNG) metamaterial, is proposed to address these inherent problems. It is demonstrated that benefitting from the large propagation attenuation introduced by the proposed meta-diaphragm, a good shielding performance can be achieved at the first higher order resonance mode. Moreover, by elaborately embedding resistors in the meta-diaphragm, the additional interferences at other resonance frequencies can be significantly suppressed. Furthermore, the structure of the meta-diaphragm is optimized with two approaches: evolving from the single-band SNG structure to the double-band one and integrating with the parasitic patches. The two approaches enable its independent controlling ability at different frequency ranges and enhancement of low-frequency shielding performance. Based on the proposed technique, an experiment is implemented to suppress the resonance-dominated interference between the passive traces in a shielding can. The result agrees well with those from theoretical prediction. The final experimental shielding can’s profile and width are 5 and 20 mm, respectively; the meta-diaphragm’s profile is only 1.5 mm, i.e., < 1/3 of shielding can’s profile, and its electrically length is only 3 mm, i.e., $0.1\lambda _{0}$ at 10 GHz, and the measured (simulated) meta-diaphragm can significantly suppress radio frequency interference (RFI) within 10.2–17 GHz (10–16.9 GHz) and realize $< -33.5$ dB ($< -40$ dB) coupling within 100 kHz–18 GHz (dc-to-20 GHz). The miniaturized dimension and wideband results indicate that our proposed technique could meet the compartment shielding requirement of a series of miniaturized packages in the practical radio frequency (RF) chip modules and circuits. [ABSTRACT FROM AUTHOR]

Radio Frequency Interference (RFI) has become a critical issue in radio astronomy observation in recent years. Compact Astronomical Low-Cost Instrument for Spectroscopy in Transportable Observatories (CALLISTO) is a worldwide network of spectrometer system for solar activity monitoring. The detection of solar radio bursts is being interrupted due to RFI as these stations are held on the ground. Since RFI signals are being detected from surrounding, this gives inaccurate data for ground base station observations. RFI sources are mostly from man-made devices. This paper study analysis of RFI level at selected CALLISTO stations by using Kurtosis Analysis. Data from Banting, Malaysia, Sumedang, Indonesia, Ooty, India and Daejeon, South Korea stations for two months (45–870 MHz) that contain solar burst and no solar burst had been selected. The kurtosis value is then compared with the population density to get the relationship between the population density and the RFI level. The highest average of kurtosis value indicates the lowest of radio frequency interference at these sites. MATLAB software was used for the data analysis and Microsoft Excel for the RFI profiling graphing. The results show that the high population density is not a main factor that contributes the interference towards the surrounding.

In this article, a spectral polarimetric filter named moving spectral depolarization ratio (MsDR) filter is proposed to mitigate the effects of clutter and noise for dual-polarization weather radar without cross-polar measurements. The filter is based on a spectral polarimetric variable termed as spectral depolarization ratio (sDR). sDR can be estimated by any polarimetric weather radar, making the MsDR filter widely applicable. Taking advantage of the spectral polarimetric features and the range-Doppler continuity of precipitation, the MsDR filter is implemented in the range-Doppler spectrogram. The performance of the MsDR filter is assessed using data collected by 1) an X-band full-polarimetric radar interfered by narrow-band clutter (both stationary and moving) and sidelobe wind turbine clutter; 2) a C-band operational dual-polarization radar affected by radio frequency interference. In addition, the implementation of the MsDR filter is straightforward, and the computational complexity is relatively low. Hence, the MsDR filter has great potential to be applied in real-time for operational radar systems at different frequencies.

CN006 is the name given by the European Space Agency (ESA) in their reports on radio frequency interference (RFI) to an instance of RFI in the L-band spectral window at 1.413 GHz protected for passive use only. The source of the interference is located east of the Chinese city of Hangzhou in an area with radar installations. Its effect was initially indistinguishable from that of many such RFI sources observed by the ESA's SMOS and NASA's Soil Moisture Active Passive (SMAP) radiometers over China. However, in July 2020, the level of radiation increased dramatically reaching levels exceeding 1700000 K as reported by Soil Moisture and Ocean Salinity (SMOS) becoming the strongest source observed by SMOS. This article describes an analysis of this very strong source (a radar). It provides information to provide insight into an example of a well-defined source of RFI and illustrates the powerful capability of the SMAP radiometer receiver and RFI processing incorporated in it for identifying and understanding interference.

The soil moisture active/passive (SMAP) satellite microwave radiometer has been providing global measurements of L-band thermal emission from Earth since April 2015. Although the radiometer operates in the protected 1400–1427 MHz portion of the radio spectrum, its measurements are still corrupted by either radio frequency interference (RFI) from out-of-band emissions via legal sources or by sources operating in-band illegally. The SMAP radiometer includes a digital backend that enables implementation of multiple ground-based RFI detection and filtering algorithms. This data is used to collect statistics and trends of Earth's RFI environment. This article examines properties of the global RFI environment as observed by SMAP, including information on RFI source properties (obtained from analysis of SMAP multiple detector outputs) and the evolution of the RFI environment in time. Residual RFI contributions after the application of SMAP RFI processing are also examined as preliminary information for the development of future methods to address their effect.

Radio frequency interference (RFI) is a risk for microwave radiometers due to their requirement of very high sensitivity. The Soil Moisture Active Passive (SMAP) mission has an aggressive approach to RFI detection and filtering using dedicated spaceflight hardware and ground processing software. As more sensors push to observe at larger bandwidths in unprotected or shared spectrum, RFI detection continues to be essential. This article presents a deep learning approach to RFI detection using SMAP spectrogram data as input images. The study utilizes the benefits of transfer learning to evaluate the viability of this method for RFI detection in microwave radiometers. The well-known pretrained convolutional neural networks, AlexNet, GoogleNet, and ResNet-101 were investigated. ResNet-101 provided the highest accuracy with respect to validation data (99%), while AlexNet exhibited the highest agreement with SMAP detection (92%).

The increasing terrestrial and space-borne communications are causing major problems to the radio astronomy observations. Only a minor part of the frequencies is allocated to the passive services, such as Radio Astronomy Services (RAS). There are only a few, relatively narrow frequency bands below 20 GHz, which are still suitable for the radio astronomical observations. In addition, Out-of-Band (OoB) emissions will be a real threat to the observations on these bands. On behalf of all European radio astronomers, the Committee on Radio Astronomy Frequencies (CRAF) of the European Science Foundation (ESF) coordinates activities to keep the frequency bands used by radio astronomy and space sciences free of interference. Along with interference caused by active radio communication services, the local electronic device selection should be considered in the observatories. For instance, more common LED based lamps could cause harmful interference for the observations. Thus, it is very important to perform continuous radio frequency interference (RFI) monitoring locally, in each radio observatory. [ABSTRACT FROM AUTHOR]

A system level methodology is presented for soft failure detection and analysis in small unmanned aerial vehicle (UAV) system, induced by radio frequency interference (RFI). This method includes a radio frequency test system based on the equivalent dynamic test method, and soft failure detection and identification method based on ground control station observation records and system log analysis. By designing signal forwarding system for global positioning system, the equivalent dynamic flight state of the UAV is realized. Through the application of standard radiated susceptibility test methods, the effects of RFI with multiple interference levels covering the frequency range from 2 MHz to 3 GHz on small UAV systems have been investigated. RFI is coupled into the UAV system through the antenna coupling and cable coupling. Soft failure modes are classified and failure analysis is mainly carried out through system log analysis, combined with full-wave simulation analysis, the root cause of the soft failure is determined. The understanding of the soft failure mode and susceptibility is beneficial to the design of UAV system, improving the robustness of software and hardware. [ABSTRACT FROM AUTHOR]

Recently, radio frequency interference (RFI) poses a growing threat to 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 article, we propose an effective and robust RFI source localization approach combined with the joint sparse recovery (JSR) theory using multisnapshot data. The JSR method uses 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 multisnapshot 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 multisnapshot data (MSOMP) to localize RFI sources. Results on synthetic data and real satellite data show that compared with the previous existing approaches, the proposed method has a better performance on the mitigation of localization accuracy bias, especially for a low BT value range case and a mixed BT value range case with multiple RFI sources. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Earth Exploration Satellite Service (EESS) for passive sensing has a primary frequency allocation in the 1400–1427 MHz band. All emissions unauthorized in this band are called RFI (Radio Frequency Interference). The SMOS (Soil Moisture and Ocean Salinity) mission is greatly perturbed by RFI impeding ocean salinity retrieval, especially in coastal areas such as the SCS (South China Sea), where the observed data has been discarded massively. At present, there is no way to eliminate the RFI impact on the retrieved salinity, other than by detecting and shutting down the emissions from the sources. However, it may be effective in a scientific sense if RFI can be quantified and applied to the salinity retrieval process. Therefore, this study proposes an RFI measuring method that can investigate contamination in both prominent and moderate respects, aroused either by on-site emissions or nearby continents. Based on the proposed method, two levels of hierarchical RFI maps of the SCS region, including the separated one and the merged one, are presented and discussed, indicating more severe contamination in northern and western SCS. Moreover, to verify the generalization of the method on open oceans far from continents, an area in the middle central Pacific is selected and tested. The result shows few or no RFI in this unattended region, which is consistent with the authors' knowledge. This study presents the concept of the "RFI map" to describe the contamination, which will hopefully help researchers comprehend the RFI state intuitively and assist in ocean salinity retrieval statistically. [ABSTRACT FROM AUTHOR]

Radio spectrum bands allocated to spaceborne synthetic aperture radar (SAR) imagery are shared by multiple missions. In practical radio spectrum environments, these bands are also used by some ground radars, e.g., C-band weather radar. Due to this fact, radio frequency interference (RFI) may occur for a spaceborne SAR when its received signals contain the transmitted waveforms from another SAR or radar operating at the same frequency band. This particular class of RFI is usually linear-frequency-modulation (LFM) signals, which can cause bright radiometric artifacts in focused SAR images. Most existing signal processing approaches designed for addressing this problem belong to the class of preprocessing methods, which removes RFI in level-0 raw radar data before SAR focusing. In this article, we propose a postprocessing kernel—2-D SPECtral ANalysis (2-D SPECAN) filter, for removing the class of LFM RFI in level-1 SLC images. The filtering consists of three main steps: Step 1: focus LFM RFI artifacts in SLC images as point-like responses in the spectral domain via 2-D SPECAN; Step 2: perform 2-D notch filtering in the spectral domain to remove the most contribution of the RFI responses; and Step 3: transform the filtered spectrum back into the SLC image domain using the inverse operation of the 2-D SPECAN. For computation efficiency, we design a simplified processing flow and adopt a blockwise processing strategy. Experiments with several Sentinel-1 SLC images demonstrate that severe RFI artifacts in SLC images can be removed significantly by the proposed method. [ABSTRACT FROM AUTHOR]

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 https://github.com/huizhangyang/BSF. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation. [ABSTRACT FROM AUTHOR]

Airborne 500–2000-MHz radiometric measurements recorded in high-latitude regions (portions of Greenland, Antarctica, and Northern Canada) are used to provide insight into the radio frequency interference (RFI) environment that can be expected for future radiometer missions operating in this frequency range. Statistics of the RFI environment are reported in terms of the likelihood of a specified bandwidth measurement containing RFI corruption. The results indicate that wideband radiometry from 500 to 2000 MHz is viable for science applications in the majority of the regions surveyed. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) sources pose threats to wideband synthetic aperture radar (SAR) systems and accurate SAR image interpretation. Since most of RFI sources are narrowband, notch filtering is a simple but effective method for RFI suppression. In this letter, a modified two-step notch filtering approach combined with linear prediction is proposed to improve the SAR image quality. The notch filtering is used to mitigate narrowband RFI energy, while the linear prediction is introduced to recover the missing range spectral component of the SAR raw data from the desired scene, which is removed together with RFI sources by the notch filter. Because of the Gibbs phenomenon in Fourier series, the small residual RFI energy after notch filtering is enough to cause image visual disturbances and affect the accuracy of the following missing range spectrum extrapolation. The two-step notch filtering with a limited bandwidth is applied for better RFI sources mitigation. Simulation results on both simulated targets and real SAR raw data validate the proposed approach. [ABSTRACT FROM AUTHOR]

The C-band Sentinel-1 synthetic aperture radar (SAR) images are affected by radio frequency interference (RFI), and this article proposes an RFI detection and localization method for these images. First, a detailed analysis of RFI based on the generation mechanism is provided, which shows that RFI in the ground range detected (GRD) products differs from common backscattering not only in frequency spectrums but also in radiometric characteristics. Then, an RFI index (RFII) is proposed for RFI detection, which takes full advantage of unique RFI characteristics in dual-polarization GRD images. Finally, both detection results in ascending and descending passes are used to locate the ground RFI sources, assuming that the presence of RFI is persistent. Theoretical analyses show that the location area is a diamond of approximately 88.76 km2. Experimental results demonstrate that the proposed method performs quite well on Sentinel-1 GRD data. It provides a convenient and tractable tool for assessing Sentinel-1 data quality as well as for monitoring RFI. Thus, Sentinel-1 measurements can be used to monitor C-band RFI, which is an important task of electromagnetic spectrum management. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) can significantly pollute synthetic aperture radar (SAR) data and images, which is also harmful to SAR interferometry (InSAR) for retrieving elevational information. To address this issue, in recent years, a class of advanced RFI suppression methods has been proposed based on narrowband properties of RFI and sparsity assumptions of radar echoes or target reflectivity. However, for SAR echoes and the associated scene reflectivity, these assumptions are usually not feasible when the imaged scene is spatially extended. In view of these problems, this study proposes an InSAR-based RFI suppression method for the case of extended scenes. For this task, we combine the RFI-polluted SAR data with RFI-free interferometric data to form an interferometric SAR data pair. We show that such an InSAR data pair embeds an interferogram having the image amplitude multiplying by a complex exponential interferometric phase. We treat the interferogram as a kind of natural image and use discrete Fourier cosine transform (DCT) for its sparse representation. Then combining the DCT-domain sparsity with low-rank modeling of RFI, we retrieve the interferogram and reconstruct the SAR image via joint low-rank and sparse optimization. Numerical simulations show that the proposed method can effectively recover SAR images and interferometric phases from RFI-polluted SAR data. [ABSTRACT FROM AUTHOR]

As the radio spectrum available to spaceborne synthetic aperture radar (SAR) is restricted to certain limited frequency intervals, there are many different spaceborne SAR systems sharing common frequency bands. Due to this reason, it is reported that two spaceborne SARs at orbit cross positions can potentially cause severe mutual interference. Specifically, the transmitting signal of an SAR, typically linear frequency modulated (LFM), can be directly received by the side or back lobes of another SAR’s antenna, causing radiometric artifacts in the focused image. This article tries to model and characterize the artifacts and study efficient methods for mitigating them. To this end, we formulate an analytical model for describing the artifact, which reveals that the mutual interference can introduce a 2-D LFM radiometric artifact in image domain with a limited spatial extent. We show that the artifact is low-rank based on a range–azimuth decoupling analysis and 2-D high-order Taylor expansion. Based on the low-rank model, we show that two methods, i.e., principal component analysis and its robust variant, can be adopted to efficiently mitigate the artifact via processing in the image domain. The former method has the advantage of fast processing speed, for example, a subswath of Sentinel-1 interferometric wide swath image can be processed within 70 s via blockwise processing, whereas the latter provides improved accuracy for sparse pointlike scatterers. Experiment results demonstrate that the radiometric artifacts caused by mutual interference in Sentinel-1 level-1 images can be efficiently mitigated via the proposed methods. [ABSTRACT FROM AUTHOR]

In radio astronomy observations, radio frequency interference (RFI) is mixed into the telescope receiving system in various forms. The existence of RFI brings misjudgment to the observation or reduces the observational signal-to-noise ratio. In recent years, the domestic and overseas radio astronomy has developed rapidly. Large-scale radio telescopes and telescope arrays at home and abroad have been constructed successively. Observation sensitivity is greatly improved, and the influence of RFI is particularly prominent. With the development of science and technology and the intensification of human activities, the RFI has become increasingly serious and irreversible. We propose to use the two-dimensional discrete wavelet transform method to analyze the data of radio astronomy observations, and to perform wavelet transform on the time-frequency sequence output by the telescope system. According to the wavelet coefficients, the every component in the original signal is separated, and the corresponding threshold value is obtained by the statistics on each component. Each component is compared with the threshold to identify the interference component, and to mark it for removal. This method is used to process the actual observation data. The results show that this method can well mark and eliminate the interference signal, and improve the observational signal-to-noise ratio. [ABSTRACT FROM AUTHOR]