Effect Of Radio Frequency Interference Research Articles

Statistical analysis and effects of radio frequency interference in GPS signal quality in Thailand

The radio frequency interference (RFI) in global navigation satellite system (GNSS) signals has recently received much attention in the GNSS community because of frequent jamming issues. The carrier-to-noise density ratio (C/N0) is one of the common parameters to indicate the signal quality. In this work, we propose a real-time RFI analysis based on windowing and normalization of C/N0 observations. Specifically, the percentage of RFI values are analyzed based on the modified RFI detection. The steps to analyze the RFI levels (low, medium, high) are highlighted. In addition, we analyzed the occurrences of local RFI effects in areas surrounding the Suvarnabhumi International Airport as well as remote areas. We validate the modified RFI detection by using the GNSS reference stations at the urban, suburban, and outside the capital city in Thailand. The user positioning errors with the high (severe) RFI levels are investigated based on the single point positioning (SPP) and real-time kinematics (RTK). From the experimental simulations, the high RFI levels at the urban are higher than those at the suburban. As expected, the statistical analysis covering COVID-19 (2019 to 2023) shows that the high RFI levels in June 2023 (post COVID-19) are more than those in June 2020 and 2021 (lockdown COVID-19) by about twofold. Additionally, the SPP positioning errors with the medium/high RFI levels are clearly seen. There are more floating solutions in the RTK system in the year with more RFI presence.

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.

Soil moisture mapping from SMOS: evaluating the accuracy of the operational product in a Mediterranean setting

ABSTRACT The present study provides a thorough evaluation of the SMOS surface soil moisture (SSM) product in a typical Mediterranean setting in Greece. For this purpose, a total of 4 agricultural sites were used for which co-orbital in-situ measurements from ground SSM sensors were available for year 2020. In this context, the effect of topographical and geomorphological features, land use/cover and the satellite orbit type and the Radio Frequency Interference (RFI) were also examined. A series of statistical metrics were computed, which allowed evaluating the agreement between the 2 datasets. In overall, results showed a reasonable agreement in specific land use/cover types between the SMOS product and the corresponding in-situ measurements obtained from the 0–5 cm soil moisture layer. In most cases, Root Mean Square Difference (RMSD) was close to 0.15 m3 m−3 (minimum 0.126 m3 m−3). Tomato and vineyard showed a satisfactory agreement compared to walnut and cotton crops. The autumn period had the highest agreement for tomato crop. The effect of RFI was also quite noticeable, as after the exclusion of pixels with high RFI, statistical agreement was noticeably improved. This study is, to our knowledge, one of the few that investigates in a Greek setting the accuracy of the SMOS product. The study results can contribute to the understanding of the practical value of the SMOS product in agricultural and arid/semi-arid Mediterranean environments while support efforts ongoing globally aiming at improving the SMOS SSM product accuracy.

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype.

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector.

STARFIRE: An algorithm for estimating radio frequency interference in orbits around Earth

Ground-based 21-cm experiments targeting the global signal from the periods of Cosmic Dawn (CD) and Epoch of Reionization (EoR) are susceptible to adverse effects presented by i) the ionosphere ii) antenna chromaticity induced by objects in its vicinity iii) terrestrial radio frequency interference (RFI). Terrestrial RFI is particularly challenging as the FM radio band spanning over 88-108 MHz lies entirely within the frequency range of the CD/EoR experiments (∼40–200 MHz). Multiple space-based experiments have been proposed to operate in the radio-quiet zone on the lunar farside. An intermediate option in cost and complexity is an experiment operating in space in an orbit around Earth, which readily alleviates the first two challenges. However, the effect of RFI in Earth’s orbit on the detection of global signal needs to be quantitatively evaluated. We present STARFIRE – Simulation of TerrestriAl Radio Frequency Interference in oRbits around Earth – an algorithm that provides an expectation of FM seeded RFI at different altitudes over Earth. Using a limited set of publicly available FM transmitter databases, which can be extended by the user community, we demonstrate the use of the STARFIRE framework to generate a three-dimensional spatio-spectral cube of RFI as would be measured in Earth orbit. Applications of STARFIRE include identifying minimum RFI orbits around Earth, producing RFI spectra over a particular location, and generating RFI heatmaps at specific frequencies for a range of altitudes. STARFIRE can be easily adapted for different frequencies, altitudes, antenna properties, RFI databases, and combined with astrophysical sky-models. This can be used to estimate the effect of RFI on the detection of global 21-cm signal from Earth-orbit, and hence for sensitivity estimates and experiment design of an Earth orbiting CD/EoR detection experiment.

Characterization of inpaint residuals in interferometric measurements of the epoch of reionization

ABSTRACTTo mitigate the effects of Radio Frequency Interference (RFI) on the data analysis pipelines of 21 cm interferometric instruments, numerous inpaint techniques have been developed. In this paper, we examine the qualitative and quantitative errors introduced into the visibilities and power spectrum due to inpainting. We perform our analysis on simulated data as well as real data from the Hydrogen Epoch of Reionization Array (HERA) Phase 1 upper limits. We also introduce a convolutional neural network that is capable of inpainting RFI corrupted data. We train our network on simulated data and show that our network is capable of inpainting real data without requiring to be retrained. We find that techniques that incorporate high wavenumbers in delay space in their modelling are best suited for inpainting over narrowband RFI. We show that with our fiducial parameters discrete prolate spheroidal sequences (dpss) and clean provide the best performance for intermittent RFI while Gaussian progress regression (gpr) and least squares spectral analysis (lssa) provide the best performance for larger RFI gaps. However, we caution that these qualitative conclusions are sensitive to the chosen hyperparameters of each inpainting technique. We show that all inpainting techniques reliably reproduce foreground dominated modes in the power spectrum. Since the inpainting techniques should not be capable of reproducing noise realizations, we find that the largest errors occur in the noise dominated delay modes. We show that as the noise level of the data comes down, clean and dpss are most capable of reproducing the fine frequency structure in the visibilities.

A framework for estimating all-weather fine resolution soil moisture from the integration of physics-based and machine learning-based algorithms

Due to the effects of radio frequency interference and the limitations of algorithms under specific conditions, most of the currently available microwave-based soil moisture (SM) products are spatially discontinuous and have coarse spatial resolution, whereas optical observations also reveal various data gaps due to cloud contamination. Hence, the prediction of SM over invalid pixels and disaggregation from coarse to high scales are two main processes for obtaining SM at fine spatiotemporal resolution (e.g., daily/1-km). In the present study, two methods with respect to disaggregation-first or prediction-first were investigated from the synergetic use of the widely recognized European Space Agency-Climate Change Initiative (ESA-CCI) SM product and Moderate Resolution Imaging Spectroradiometer (MODIS) images over the Tibetan Plateau (TP) region. Specifically, the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) algorithm and the generalized regression neural network (GRNN) were implemented in the disaggregation and prediction, respectively. In DisPATCh, spatially complete land surface temperature (LST), normalized difference vegetation index (NDVI) and digital elevation model (DEM) were provided as essential inputs to downscale the microwave-based ESA-CCI to a spatial resolution of 1 km, whereas MODIS-derived LST, NDVI, land surface albedo and DEM were considered in the GRNN prediction. Following the two methods, the daily/1-km SM dataset over a period of three years was finally estimated. Assessments with ground in-situ SM measurements over the TP region reveal an acceptable accuracy with unbiased root mean square errors of ∼ 0.06 m3/m3, indicating the potential to obtain operational daily/1-km spatially continuous SM products in future developments.

Global-scale biomass estimation based on machine learning and deep learning methods

Modeling accurate aboveground biomass (AGB) is a critical aspect of remote sensing research. Besides selecting the appropriate model from significant inputs, integrating optical and microwave products can improve biomass estimation accuracy. Three models were used to estimate biomass to accomplish these goals: Convolutional Neural Network (CNN), Random Forest (RF), and Support Vector Machine for Regression (SVR). Two feature selection techniques, Support Vector Machine for Regression Feature Selection (SVRFS) and Random Forest Feature Selection (RFFS), were used to select the most significant variables to include in the AGB estimation models. Grid search with cross-validation was employed to optimize the hyperparameters for each algorithm. This research aimed to reduce the effects of Radio Frequency Interference (RFI), where RFI filtering outperformed the remaining three models (R2 values were 0.79, 0.80, and 0.82 without reducing RFI effects and were 0.85, 0.87, and 0.88 with reducing RFI effects for SVR, RF and CNN models respectively). Two feature selection techniques jointly selected the variables tree height, vegetation optical depth (VOD), and normalized difference vegetation index (NDVI). The highest Root-Mean-Square deviation (RMSE) values were obtained when CNN and RF models were used in conjunction with the SVRFS method (31.22 Mg/ha and 31.28 Mg/ha for CNN and RF models using the SVRFS model, respectively). This study investigated machine learning and deep learning algorithms’ capability to improve global-scale AGB estimation.

Redesign Driven by Manufacturing Data for Next-Generation Modernization of Legacy Products

Abstract As the life cycles of many products advance, obsolescence of original components can pose challenges for a product’s maintainability and serviceability. Information about the original design intent of the product system may not be available. For example, it may not be obvious how to identify old components by reverse engineering or to select and configure the optimal components for best system performance. Thus, time spent to redesign an older legacy product with high-fidelity simulations may be difficult to justify. Increasingly more performance measurements are available in digital twins of product systems generated during manufacturing and testing. This paper introduces a redesign from manufacturing data mapping (RfMDM) method that can infer optimal new components to modernize legacy product systems. This RfMDM method does not rely upon time-consuming redesign efforts. The inputs come from a basic understanding of the subsystem of concern and qualitative scoring estimates of the influence of design rules on design criteria and simulation fidelity. RfMDM transforms manufacturing data to determine the significance and required quality of each design variable of each obsolete component. This enables estimation of optimal new component specifications for selection and a first article redesign and test to enable product line improvements over time more efficiently. Recognizing that radiofrequency interference effects in printed circuit boards present a complex and quintessential example of legacy subsystems with potentially obsolete components, this paper demonstrates the RfMDM method with a theoretical example subsystem of an active low-pass filter on a printed circuit board.

Detection and Identification of System Level Soft Failure Induced by Radio Frequency Interference in Small UAV System

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.

Performance Analysis Techniques for Real-Time Broadband RFI Filtering System of uGMRT

Electromagnetic radiation from human activities, known as man-made Radio Frequency Interference (RFI), adversely affects radio astronomy observations. In the vicinity of the Upgraded Giant Metrewave Radio Telescope (uGMRT) array, the sparking on power lines is the major cause of interference at observing frequencies less than 800[Formula: see text]MHz. A real-time broadband RFI detection and filtering system is implemented as part of the uGMRT wideband signal processing backend to mitigate the effect of broadband RFI. Performance analysis techniques used for testing and commissioning the system for observations in the beamformer and correlator modes of the uGMRT are presented. The concept and implementation of recording simultaneous unfiltered and filtered data along with data analysis and interpretation is illustrated using an example. For the beamformer mode, spectrogram, single spectral channel, and its Fourier transform is used for performance analysis whereas, in the correlator mode, the cross-correlation function, closure phase, and visibilities from the simultaneously recorded unfiltered and filtered is carried out. These techniques are used for testing the performance of the broadband RFI filter and releasing it for uGMRT users.

The FAST Ultra-Deep Survey (FUDS): Observational strategy, calibration and data reduction

Abstract The FAST Ultra-Deep Survey (FUDS) is a blind survey that aims for the direct detection of H i in galaxies at redshifts $z<0.42$ . The survey uses the multibeam receiver on the Five-hundred-metre Aperture Spherical Telescope (FAST) to map six regions, each of size $0.72\ \textrm{deg}^2$ at high sensitivity ( ${\sim}50\,\mu \textrm{Jy}$ ) and high-frequency resolution (23 kHz). The survey will enable studies of the evolution of galaxies and their H i content with an eventual sample size of ${\sim}1\,000$ . We present the science goals, observing strategy, the effects of radio frequency interference at the FAST site, our mitigation strategies and the methods for calibration, data reduction and imaging as applied to initial data. The observations and reductions for the first field, FUDS0, are completed, with around 128 H i galaxies detected in a preliminary analysis. Example spectra are given in this paper, including a comparison with data from the overlapping GAL2577 field of Arecibo Ultra-Deep Survey.

An HI intensity mapping survey with a Phased Array Feed

We report results from a neutral hydrogen (H i) intensity mapping survey conducted with a Phased Array Feed (PAF) on the Parkes telescope. The survey was designed to cover ∼ 380 deg2 over the redshift range 0.3 < z < 1 (a volume of ∼ 1.5 Gpc3) in four fields covered by the WiggleZ Dark Energy Survey. The results presented here target a narrow redshift range of 0.73 < z < 0.78 where the effect of radio frequency interference (RFI) was less problematic. The data reduction and simulation pipeline was described, with an emphasis on flagging of RFI and correction for signal loss in the data reduction process, particularly due to the foreground subtraction methodology. A cross-correlation signal was detected between the H i intensity maps and the WiggleZ redshift data, with a mean amplitude of ⟨ΔT b δ opt⟩ = 1.32 ± 0.42 mK (statistical errors only). A future Parkes cryogenic PAF is expected to detect the cross-correlation signal with higher accuracy than previously possible and allow measurement of the cosmic H i density at redshifts up to unity.

Quantifying excess power from radio frequency interference in Epoch of Reionization measurements

ABSTRACT We quantify the effect of radio frequency interference (RFI) on measurements of the 21-cm power spectrum during the Epoch of Reionization (EoR). Specifically, we investigate how the frequency structure of RFI source emission generates contamination in higher order wave modes, which is much more problematic than smooth-spectrum foreground sources. Using a relatively optimistic EoR model, we find that even a single relatively dim RFI source can overwhelm the EoR power spectrum signal of $\sim 10\, {\rm mK}^2$ for modes $0.1 \ \lt k \lt 2 \, h\, {\rm Mpc}^{-1}$. If the total apparent RFI flux density in the final power spectrum integration is kept below 1 mJy, an EoR signal resembling this optimistic model should be detectable for modes $k \lt 0.9\, h\, {\rm Mpc}^{-1}$, given no other systematic contaminants and an error tolerance as high as 10 per cent. More pessimistic models will be more restrictive. These results emphasize the need for highly effective RFI mitigation strategies for telescopes used to search for the EoR.

Direct RF Sampling Hyperspectral Microwave Radiometer (DSμRAD) for Ground Use

A microwave radiometer (MWR) is an instrument used to measure the microwave power emitted from the natural origin as a brightness temperature ( T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> ). A more active use of microwaves below the Ka-band is being planned for wireless communication in the near future, thereby increasing the risk of artificial radio frequency interference (RFI) with natural origin T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> measurement. Although measuring T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> values in high-frequency resolution is an effective way to detect and remove the effect of RFI from measured T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> , it is difficult to fundamentally increase the number of simultaneously measurable T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> in the conventional MWR due to space issues. Therefore, we developed a new MWR - direct RF sampling hyperspectral microwave radiometer (DS μRAD) that adopts multiple A/D converters of 10-GSPS order [samples per second (SPS)] to digitize microwave power received by an ultrawideband antenna. DS μRAD can measure the microwave power of the vertical and horizontal polarizations to decompose each into a T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> spectrum from 512 MHz to 40.96 GHz at 16-MHz intervals. We expect that DS μRAD will open up new possibilities for microwave remote sensing, including a drastic response to increased RFI risk, i.e., effective detection and removal (or extraction) of RFI. This letter presents the DS μRAD design in detail and the initial evaluation results of the instrument's performance.

Implementation of a Testbed for GNSS-R Payload Performance Evaluation

The functional performance of space-borne instruments must be validated on ground before and after satellite integration. The effects of radio-frequency interference are also becoming more important, even in protected bands for earth observation. In this article, a GNSS and GNSS-R signal simulator is developed as part of a testbed of GNSS receivers and GNSS-R payloads' performance in high dynamics, and to study the effects of RFI in the GNSS-R observables. This article describes the different concepts and key enabling techniques that have been developed to support this project.

A New Restoration Method for Radio Frequency Interference Effects on AMSR-2 over North America

Observations from spaceborne microwave imagers are important sources of land surface information. However, the low-frequency channels of microwave imagers are easily interfered with by active radio signals with similar frequencies. Radio frequency interference (RFI) signals are widely distributed because of the lack of frequency protection, which seriously hinders the application of microwave imager data in data assimilation and retrieval research. In this paper, a new data restoration method is proposed based on principal component analysis (PCA). Both the ideal and real reconstruction experiments show that the new method can effectively repair abnormal observations interfered by RFI compared with the commonly used Cressman interpolation method because observation information over the whole selected domain is used for restoration in the new method, whereas Cressman interpolation uses only a selection of data around the target observation. The observation errors in the data with RFI can be reduced by one order of magnitude by means of the new method and little artificial information is introduced. One-week restoration validation also proves that the new method has a stable accuracy and broad application prospects.

Performance of DC SQUID Second-Order Axial Gradiometer Response to Radio Frequency Interference

A low-Tc dc SQUID placed in a superconducting and magnetic shield is insensitive to magnetic interference, but not completely immune to the effects of radio frequency interference (RFI). Here, we investigated the effect of two orthogonal RF fields on the second-order axial gradiometer's transfer characteristics and noise characteristics. RF field induced phase shift of the V – $\Phi$ curve. The modulated spikes of RF field on the V – $\Phi$ curve and noise spectrum became significant with the RF field increasing. The white noise of the system increased to 12.5 $fT/\sqrt{\text{Hz}}$ with +10 dB axial RF field, nearly one and a half times of 8.5 $fT/\sqrt{\text{Hz}}$ without RFI. The working point jumping occurred under the radial RF field of +5 and +10 dB, and the white noise increased to 17.2 and 165.8 $fT\sqrt{\text{Hz}}$ , respectively. The reason of work point drift related to the magnetic flux coupling between the SQUID and pickup coil needs further studies.

An RFI Detection and Mitigation Algorithm for an L-Band Phased Array Radiometer

To monitor sea surface salinity (SSS) in coastal zones with high temporal and spatial resolution, an L-band phased array radiometer is developed. However, it is well known that the L-band radiometers suffer from serious radio frequency interference (RFI) effects. To mitigate the RFI effects, an RFI detection and mitigation algorithm is proposed for the L-band phased array radiometer. The experiments in coastal zones are performed to assess the performance of the RFI algorithm. The results indicate that the detection and mitigation algorithm exhibits good performance and can effectively mitigate the RFI effects.

Real-Time RFI Processor for Future Spaceborne Microwave Radiometers

Anthropogenic radio frequency interference (RFI) within microwave radiometer bands is a serious problem in remote sensing. The problem is ever-increasing and thus, future spaceborne microwave radiometers will require efficient methods to mitigate the effects of RFI. In this paper, we present a solution: an RFI processor to detect and blank the RFI in real time. The processor was designed to be compatible with the 18.7-GHz channel of the Microwave Imager Instrument onboard the European MetOp Second Generation satellite system. The RFI processor developed here applies the following complementary detection algorithms: 1) anomalous amplitude detection; 2) kurtosis; and 3) two different cross-frequency algorithms. In the processing, the data are divided into subsamples with fine temporal and frequency resolution. The RFI processor detects and filters out RFI with this fine resolution in real time and then integrates the clean (noncontaminated) subsamples over time and frequency. Thus, a cleaned version of the radiometer data can be downlinked at a traditional low data rate. The processing is implemented in a reprogrammable field programmable gate array with high processing capacity, which provides high flexibility. The processing bandwidth that is applied is 200 MHz (+ 25-MHz transition bands on both sides). The detection limits of the system vary between various kinds of RFI but are in line with simulations. The power consumption of the RFI processor is ∼12 W (at room temperature), and its mass is ∼1.1 kg.