Radio Frequency Interference Signals Research Articles

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.

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

We present deep upper limits from the 2014 Murchison Widefield Array Phase I observing season, with a particular emphasis on identifying the spectral fingerprints of extremely faint radio frequency interference (RFI) contamination in the 21 cm power spectra (PS). After meticulous RFI excision involving a combination of the SSINS RFI flagger and a series of PS-based jackknife tests, our lowest upper limit on the Epoch of Reionization (EoR) 21 cm PS signal is Δ2 ≤ 1.61 × 104 mK2 at k = 0.258h Mpc−1 at a redshift of 7.1 using 14.7 hr of data. By leveraging our understanding of how even fainter RFI is likely to contaminate the EoR PS, we are able to identify ultrafaint RFI signals in the cylindrical PS. Surprisingly this signature is most obvious in PS formed with less than 1 hr of data, but is potentially subdominant to other systematics in multiple-hour integrations. Since the total RFI budget in a PS detection is quite strict, this nontrivial integration behavior suggests a need to more realistically model coherently integrated ultrafaint RFI in PS measurements so that its potential contribution to a future detection can be diagnosed.

Radio Frequency Interference Mitigation via Nonnegative Matrix Factorization for GNSS

A radio frequency interference (RFI) mitigation approach based on nonnegative matrix factorization (NMF) for global navigation satellite systems (GNSS) signals is proposed. The proposed approach employs NMF to separate the interference from the GNSS signals, and it can be deployed in a supervised or semi-blind manner. The supervised NMF framework assumes prior knowledge about the RFI whereas its semi-blind counterpart does not require any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information about the RFI. Results indicate that both schemes are able to mitigate narrow and wideband RFI signals, outperforming Kalman, notch filter and wavelet-based techniques, enabling GNSS signal acquisition even in scenarios where the interference is 50 dB stronger than the GNSS signals. In addition, the proposed approach is able to mitigate multiple, different types of RFI corrupting the received GNSS signal.

Trajectory-based RFI subtraction and calibration for radio interferometry

ABSTRACT Radio interferometry calibration and radio frequency interference (RFI) removal are usually done separately. Here we show that jointly modelling the antenna gains and RFI has significant benefits when the RFI follows precise trajectories, such as for satellites. One surprising benefit is improved calibration solutions, by leveraging the RFI signal itself. We present trajectory-based RFI subtraction and calibration (tabascal), a new algorithm that jointly models the RFI and calibration parameters in visibilities. We test tabascal on simulated MeerKAT calibration observations contaminated by satellite-based RFI. We obtain gain estimates that are both unbiased and up to an order of magnitude better constrained compared to uncontaminated data. When combined with an ad hoc RFI subtraction scheme, tabascal solutions can be further applied to an adjacent target observation: 5 min of calibration data results in an image with about a third the noise achieved when using flagging alone. The recovered flux distribution of RFI subtracted data was on par with uncontaminated data. In contrast, RFI flagging alone resulted in a higher detection threshold and consistent underestimation of source fluxes. For a mean RFI amplitude of 17 Jy, using RFI subtraction leads to less than 1 per cent loss of data compared to 75 per cent data loss from an ideal 3σ flagging algorithm, a very significant increase in data available for science analysis. Although we have examined the case of satellite RFI, tabascal should work for any RFI moving on parametrizable trajectories, relative to the phase centre, such as planes and/or objects fixed to the ground.

Simulating Spectral Kurtosis Mitigation against Realistic Radio Frequency Interference Signals

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

Wideband Radio Frequency Interference Cancellation for High-Sensitivity Phased Array Receivers With True Time Delays and Truncated Hadamard Projection

Radio frequency interference (RFI) is a significant challenge for high-sensitivity phased array instruments. RFI can be suppressed using digital signal processing, but to improve dynamic range for wideband RFI, it can be desirable to remove interference in the analog domain before sampling. In previous work, it has been shown that analog true time delay (TTD) stages with a truncated Hadamard transform can place a wide-band spatial null on RFI from a given direction of arrival. We show that TTD and Hadamard projection is mathematically equivalent to a bank of classical narrow-band subspace projection beamformers, but with a structure that allows efficient implementation in either analog circuitry or digital hardware. We analyze how loss in the TTD blocks and time delay errors affect beamformer performance and propose methods for calibrating time delays. Simulation results show that ideal TTD and Hadamard projection matches the bank of subspace projection beamformers and places deep nulls over wideband RFI signals while achieving SNR performance comparable to the maximum signal-to-interference and -noise ratio beamformer.

Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals

In pulsar signal processing, two primary difficulties are (1) radio-frequency interference (RFI) mitigation and (2) information loss due to preprocessing and mitigation itself. Linear mitigation methods have a difficulty in RFI modeling, and accommodate a limited range of RFI morphologies. Thresholding methods suffer from manual factors and adaptability. There is also a distinct lack of methods dedicated to information loss. In this paper, a novel method “CS-Pulsar” is proposed. It carries out compressed sensing (CS) on time-frequency signals to accomplish RFI mitigation and signal restoration simultaneously. Curvelets allow an optimal sparse representation for multichannel pulsar signals containing the time-of-arrival dispersion relationship. CS-Pulsar mitigation is implemented using a regularized least-squares framework that does not require the statistics of RFI to be known beforehand. CS-Pulsar implements channel restoration, and useful signal contents are retrieved from the measurement error by a morphological component analysis aided by the root-mean-square envelope. These two steps allow CS-Pulsar to provide key signal details for special astrophysical purposes. Experiments of signal restoration for pulsar data from the Nanshan 26 m radio telescope reveal the advantage of CS-Pulsar. The method successfully removes false peaks due to on-pulse RFI in multipeaked pulsar profiles. CS-Pulsar also increases the timing accuracy and signal-to-noise ratio proving its feasibilities and prospects in astrophysical measurements.

Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties

Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments. RFI severely detracts from SAR imaging quality, which hinders image interpretation. Therefore, some RFI mitigation algorithms have been introduced based on the partial features of RFI, but the RFI reconstruction models in these algorithms are rough and can be improved further. This paper proposes two algorithms for accurately modeling the structural properties of RFI and target echo signal (TES). Firstly, an RFI mitigation algorithm joining the low-rank characteristic and dual-sparsity property (LRDS) is proposed. In this algorithm, RFI is treated as a low-rank and sparse matrix, and the sparse matrix assumption is made for TES in the time–frequency (TF) domain. Compared with the traditional low-rank and sparse models, it can achieve better RFI mitigation performance with less signal loss and accelerated algorithm convergence. Secondly, the other RFI mitigation algorithm, named as TFC-LRS, is proposed to further reduce the signal loss. The TF constraint concept, in lieu of the special sparsity, is introduced in this algorithm to describe the structural distribution of RFI because of its aggregation characteristic in the TF spectrogram. Finally, the effectiveness, superiority, and robustness of the proposed algorithms are verified by RFI mitigation experiments on the simulated and measured SAR datasets.

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

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

RFI Localization via Reweighted Nuclear Norm Minimization in Microwave Interferometric Radiometry

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

Radio Frequency Interference Characterization and Mitigation for Polarimetric Weather Radar: A Study Case

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.

On-Ground RFI Mitigation for Spaceborne Multichannel SAR Systems Using Auxiliary Beams

Radio frequency interference (RFI) is becoming a major concern for future synthetic aperture radar (SAR) missions due to the increased user demand for frequency occupation in a number of applications. Each occurrence of interference introduces artifacts in the radar imagery, biasing the measurements and leading to erroneous results. In addition to conventional techniques, the use of multichannel SAR for RFI mitigation has been proposed, because its digital beamforming (DBF) capability allows for a spatial filtering of the received signals. Thereby, it becomes possible to remove RFI that arrives from a different direction than the SAR signal. Past publications on the topic presented highly flexible spatial filtering techniques. Those methods require either additional on-board processing or a substantial increase in the downlink capacity. This article shows that by slightly reducing the flexibility of the spatial filtering, DBF can be utilized for RFI mitigation without either drawbacks: the processing is performed on-ground after downlinking the data and the data volume remains manageable. This is achieved with auxiliary beams. Their concept and limitations are discussed in detail in this article and are supported with simulated RFI mitigation results. Furthermore, it is shown that the information collected with an auxiliary beam can also be used to filter the RFI signal when it is spatially nonorthogonal to the SAR signal.

Radio Frequency Interference Signature Detection in Radar Remote Sensing Image Using Semantic Cognition Enhancement Network

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, 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 paper, 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 pixel-wise 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.

Extraction and Mitigation of Radio Frequency Interference Artifacts Based on Time-Series Sentinel-1 SAR Data

Radio frequency interference (RFI) is a critical issue for accurate remote sensing by synthetic aperture radar (SAR). Existing literature mainly detects and mitigates RFI in the raw data domain, which is generally not accessible to the end-user. In this article, a novel RFI extraction and mitigation scheme in the image domain is proposed using multitemporal analysis of SAR images. By exploiting the coupling correlation and complementary information among the time-series images, the background landscape could be modeled as relatively stationary with the low-rank property. Meanwhile, the radiometric artifacts corresponding to RFI could be well extracted and characterized by the sparse components. Extraction and mitigation of RFI signatures could be achieved simultaneously via a joint iterative optimization process. Experimental results on typical real-measured Sentinel-1 datasets acquired in different regional areas with various RFI types demonstrate the validity of the proposed method.

Detecting pulsars with neural networks: a proof of concept

ABSTRACT Pulsar searches are computationally demanding efforts to discover dispersed periodic signals in time- and frequency-resolved data from radio telescopes. The complexity and computational expense of simultaneously determining the frequency-dependent delay (dispersion) and the periodicity of the signal is further exacerbated by the presence of various types of radio-frequency interference (RFI) and observing-system effects. New observing systems with wider bandwidths, higher bit rates, and greater overall sensitivity (also to RFI) further enhance these challenges. We present a novel approach to the analysis of pulsar search data. Specifically, we present a neural-network-based pipeline that efficiently suppresses a wide range of RFI signals and instrumental instabilities and furthermore corrects for (a priori unknown) interstellar dispersion. After initial training of the network, our analysis can be run in real time on a standard desktop computer with a commonly available, consumer-grade graphics processing unit (GPU) . We complement our neural network with standard algorithms for periodicity searches. In particular with the Fast Fourier Transform and the Fast Folding Algorithm and demonstrate that with these straightforward extensions, our method is capable of identifying even faint pulsars while maintaining an extremely low number of false positives. We furthermore apply our analysis to a subset of the PALFA survey and demonstrate that in most cases the automated dispersion removal of our network produces a time series of similar quality as dedispersing, using the actual dispersion measure of the pulsar in question. On our test data, we are able to make predictions whether a pulsar is present in the data or not 200 times faster than real time.

Characteristics of the Global Radio Frequency Interference in the Protected Portion of L-Band

The National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active–Passive (SMAP) radiometer has been providing geolocated power moments measured within a 24 MHz band in the protected portion of L-band, i.e., 1400–1424 MHz, with 1.2 ms and 1.5 MHz time and frequency resolutions, as its Level 1A data. This paper presents important spectral and temporal properties of the radio frequency interference (RFI) in the protected portion of L-band using SMAP Level 1A data. Maximum and average bandwidth and duration of RFI signals, average RFI-free spectrum availability, and variations in such properties between ascending and descending satellite orbits have been reported across the world. The average bandwidth and duration of individual RFI sources have been found to be usually less than 4.5 MHz and 4.8 ms; and the average RFI-free spectrum is larger than 20 MHz in most regions with exceptions over the Middle East and Central and Eastern Asia. It has also been shown that, the bandwidth and duration of RFI signals can vary as much as 10 MHz and 10 ms, respectively, between ascending and descending orbits over certain locations. Furthermore, to identify frequencies susceptible to RFI contamination in the protected portion of L-band, observed RFI signals have been assigned to individual 1.5 MHz SMAP channels according to their frequencies. It has been demonstrated that, contrary to common perception, the center of the protected portion can be as RFI contaminated as its edges. Finally, there have been no significant correlations noted among different RFI properties such as amplitude, bandwidth, and duration within the 1400–1424 MHz band.

RFI Suppression for SAR Systems Based on Removed Spectrum Iterative Adaptive Approach

A synthetic aperture radar (SAR) system can be seriously contaminated by radio frequency systems because of working in the same microwave frequency bands, which would degrade the SAR image quality and affect the accuracy of image interpretation. In this paper, a novel radio frequency interference (RFI) suppression approach including RFI identification, band-stop filtering and a removed spectrum iterative adaptive approach (RSIAA) is proposed. First, the smoothing process is added before RFI signal detection to improve the RFI detection capacity. Afterwards, the band-stop filtering with a broaden factor is proposed to mitigate the residual RFI, and it ensures the accuracy of the following removed spectrum recovery by the RSIAA. Finally, the removed spectrum components are estimated from available adjacent spectrum data by the RSIAA in turn to obtain the desired range spectra. Compared with the conventional range frequency filtering method for RFI suppression, the capacity of the weak RFI signal detection is improved, and the increased sidelobes due to the discontinuous spectra are well suppressed. Simulation experiments on both simulated SAR raw data, Gaofen-3 and Sentinel-1 SAR raw data validate the proposed RFI suppression approach.

Cross-Validation of Radio-Frequency-Interference Signature in Satellite Microwave Radiometer Observations over the Ocean

Radio-frequency-interference (RFI) signals have gradually become a more serious problem in active and passive microwave remote sensing. However, currently, there is no reliable RFI source distribution data to evaluate the accuracy of existing RFI identification methods. In this study, a simplified generalized RFI detection method (GRDM) is proposed to detect RFI applied to the ocean surface. Two RFI detection methods, the GRDM and the double-principal component analysis (DPCA) method, are used for cross-validation to obtain RFI recognition thresholds of DPCA in the Advanced Microwave Scanning Radiometer 2 (AMSR2) ocean data. In addition, in the present work the source and distribution characteristics of RFI over the ocean surface are also analyzed. The results show that the proposed scheme can effectively identify RFI signals from AMSR2 data, and only 7.3, 10.65, and 18.7 GHz channels are contaminated by RFI over the ocean surface. There are strong 7.3 GHz interference signals over the waters of East Asia (with the value of ΔTBH mostly between 5 and 30 K and ΔTBv mostly between 5 and 40 K), Europe (with the value of ΔTBH mostly between 5 and 40 K and ΔTBv mostly between 5 and 30 K), and North America (with the value of ΔTBH mostly between 5 and 50 K and ΔTBv mostly between 5 and 30 K). The RFI signals in 10.65 GHz data are mainly distributed over the Mediterranean and other European waters (with the value of ΔTBH mostly between 5 and 35 K and ΔTBv mostly between 5 and 20 K). The RFI signals at 18.7 GHz are mainly present over the offshore marine areas of North America (with the value of ΔTBH mostly between 5 and 50 K and ΔTBv mostly between 5 and 40 K), with the strongest RFI distributed near the Great Lakes of America, and the RFI magnitudes over the east and west coasts are stronger than over the south coast. Satellite-borne microwave observations over the ocean suffer from interference mainly from stationary communication/television satellites. Due to the reflection of the sea surface, the range and intensity of RFI are strongly dependent on the relative geometric positions of stationary satellites and space-borne passive instruments. Therefore, RFI coverage area changes every day over the ocean in one 16-day period, which is very different from that over the land.

Radio frequency interference mitigation using pseudoinverse learning autoencoders

Radio frequency interference (RFI) is an important challenge in radio astronomy. RFI comes from various sources and increasingly impacts astronomical observation as telescopes become more sensitive. In this study, we propose a fast and effective method for removing RFI in pulsar data. We use pseudo-inverse learning to train a single hidden layer auto-encoder (AE). We demonstrate that the AE can quickly learn the RFI signatures and then remove them from fast-sampled spectra, leaving real pulsar signals. This method has the advantage over traditional threshold-based filter method in that it does not completely remove contaminated channels, which could also contain useful astronomical information.

A survey of spatially and temporally resolved radio frequency interference in the FM band at the Murchison Radio-astronomy Observatory

AbstractWe present the first survey of radio frequency interference (RFI) at the future site of the low frequency Square Kilometre Array (SKA), the Murchison Radio-astronomy Observatory (MRO), that both temporally and spatially resolves the RFI. The survey is conducted in a 1 MHz frequency range within the FM band, designed to encompass the closest and strongest FM transmitters to the MRO (located in Geraldton, approximately 300 km distant). Conducted over approximately three days using the second iteration of the Engineering Development Array in an all-sky imaging mode, we find a range of RFI signals. We are able to categorise the signals into: those received directly from the transmitters, from their horizon locations; reflections from aircraft (occupying approximately 13% of the observation duration); reflections from objects in Earth orbit; and reflections from meteor ionisation trails. In total, we analyse 33 994 images at 7.92 s time resolution in both polarisations with angular resolution of approximately 3.5$^{\circ}$, detecting approximately forty thousand RFI events. This detailed breakdown of RFI in the MRO environment will enable future detailed analyses of the likely impacts of RFI on key science at low radio frequencies with the SKA.