Radio Frequency Interference Sources Research Articles

A low-frequency blind survey of the low Earth orbit environment using non-coherent passive radar with the Murchison widefield array

AbstractWe have extended our previous work to use the Murchison widefield array (MWA) as a non-coherent passive radar system in the FM frequency band, using terrestrial FM transmitters to illuminate objects in low Earth orbit (LEO) and the MWA as the sensitive receiving element for the radar return. We have implemented a blind detection algorithm that searches for these reflected signals in difference images constructed using standard interferometric imaging techniques. From a large-scale survey using 20 h of archived MWA observations, we detect 74 unique objects over multiple passes, demonstrating the MWA to be a valuable addition to the global Space Domain Awareness network. We detected objects with ranges up to 977 km and as small as$0.03$${\rm m}^2$radar cross section. We found that 30 objects were either non-operational satellites or upper-stage rocket body debris. Additionally, we also detected FM reflections from Geminid meteors and aircraft flying over the MWA. Most of the detections of objects in LEO were found to lie within the parameter space predicted by previous feasibility studies, verifying the performance of the MWA for this application. We have also used our survey to characterise these reflected signals from LEO objects as a source of radio frequency interference (RFI) that corrupts astronomical observations. This has allowed us to undertake an initial analysis of the impact of this RFI on the MWA and the future square kilometer array (SKA). As part of this analysis, we show that the standard MWA RFI flagging strategy misses most of this RFI and that this should be a careful consideration for the SKA.

Radio Frequency Interference Detection and Mitigation Using Compressive Statistical Sensing

AbstractQuasiperiodic radio frequency interference (RFI), such as those generated by telecommunication and active radar systems, is commonly encountered in radio astronomy observations. Such RFI‐contaminated signals contain hidden periodicities due to cyclic features involved in their formation (e.g., carrier frequencies, periodic keying of the amplitude, and baud rates). RFI signal characterization and its subsequent excision based on the well‐known cyclic spectrum analysis have been previously demonstrated; however, the high complexity of the algorithm and the computational cost of its implementation have limited its utility in radio astronomy, rendering less sophisticated solutions. To overcome this challenge, we present a novel method for RFI detection and mitigation based on efficient estimation of the cyclic spectrum by compressive statistical sensing (CSS) of sub‐Nyquist data. CSS performs second‐order statistical estimation such as cyclic spectrum using a reduced number of input samples, thereby enabling accelerated performance. To validate the feasibility of the proposed method, we conduct experiments with simulated data and assess the detection and mitigation results under different parameter settings, for example, interference‐to‐noise ratio, additional RFI sources, frequency resolution, and input data size. We demonstrate the real performance of the method by analyzing radio astronomy data (∼1.3 GHz) acquired with the L‐wide band receiver at the Arecibo Observatory, which is typically corrupted by active air surveillance radars located nearby. Our CSS‐based solution enables robust and efficient detection of the RFI frequency bands present in the L‐band data, and subsequent excision by blanking is also demonstrated.

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

Radio frequency interference (RFI) is a major issue in accurate remote sensing by a synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This paper provides a comprehensive study of the RFI mitigation techniques applicable for an SAR system. From the view of spectrum allocation, possible terrestrial and spaceborne RFI sources to SAR system and their geometry are analyzed. Typical signal models for various RFI types are provided, together with many illustrative examples from real measured data. Then, advanced signal processing techniques for removing RFI are reviewed. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends are provided from the perspective of cognitive, integrated, and adaptive. This review serves as a reference for future work on the implementation of the most suitable RFI mitigation scheme for an air-borne or space-borne SAR system.

A Subspace Algorithm to Mitigate Energy Unknown RFI for Synthetic Aperture Interferometric Radiometer

Radio frequency interferences (RFIs) are present in the data collected by a synthetic aperture interferometric radiometer (SAIR), and these would seriously affect the retrieval of brightness temperature (BT), if not properly handled. Due to the truncated Fourier characteristic, RFIs create ripples that extend over a large set of pixels of the whole image, which is difficult to cancel out their impacts at the BT domain. The current strategy relies on removing the unwanted sources at the visibility level (Fourier domain) with a set of synthetic visibilities of the RFI sources for a given snapshot. However, a major problem is that, as unauthorized emissions, the energies of RFIs are typically unknown and the estimation to the sources’ temperature could be inaccurate without credible prior information. In this article, a subspace algorithm is proposed to handle the RFI mitigation problem by exploiting RFI’s point-source characteristic, where the visibilities related to the RFIs are excluded from that of the BT scene through an orthogonal design without the need of knowing the sources’ power. Both the numerical simulations and the proof-of-concept experiment validate the theoretical correctness and the practical effectiveness of our proposed method.

Detection and Analysis of C-Band Radio Frequency Interference in AMSR2 Data over Land

A simplified generalized radio frequency interference (RFI) detection method and principal component analysis (PCA) method are utilized to detect and attribute the sources of C-band RFI in AMSR2 L1 brightness temperature data over land during 1–16 July 2017. The results show that the consistency between the two methods provides confidence that RFI may be reliably detected using either of the methods, and the only difference is that the scope of the RFI-contaminated area identified by the former algorithm is larger in some areas than that using the latter method. Strong RFI signals at 6.925 GHz are mainly distributed in the United States, Japan, India, Brazil, and some parts of Europe; meanwhile, RFI signals at 7.3 GHz are mainly distributed in Latin America, Asia, Southern Europe, and Africa. However, no obvious 7.3 GHz RFI appears in the United States or India, indicating that the 7.3 GHz channels mitigate the effects of the C-band RFI in these regions. The RFI signals whose position does not vary with the Earth azimuth of the observations generally come from stable, continuous sources of active ground-based microwave radiation, while the RFI signals which are observed only in some directions on a kind of scanning orbit (ascending/descending) mostly arise from reflected geostationary satellite signals.

Ionogram RFI Rejection Using an Autoregressive Interpolation Process

AbstractDefence Science and Technology Group's Digital Oblique Receiving System (DORS) is a direct‐digital high‐frequency ionosonde receiver, capable of collecting high‐quality ionograms simultaneously on multiple oblique ionospheric paths. One of the key signal processing steps that run on board the receiver is a novel technique for the detection and removal of radio frequency interference (RFI). In the down‐converted narrow‐band time series, external RFI sources manifest as impulsive noise, often many orders of magnitude stronger than the ionosonde signal of interest. The RFI rejection technique applies a threshold detector to the whitened data, to locate the corrupt samples, and then replaces the bad data using forward and backward linear prediction, based on an autoregressive model of the desired ionosonde signal. Algorithm performance is evaluated using a set of simple statistics and ~6,000 DORS ionograms from central Australia. Compared to a more traditional approach of clipping the strong RFI impulses, the new technique significantly reduces instances of undersuppression and recovers more of the weaker propagation mode content in the ionogram. The result is a much cleaner image for the DORS automatic feature extraction and parameterized fitting technique.

Location of Radio-Frequency Interference Sources Using the SMAP L-Band Radiometer

The Soil Moisture Active/Passive (SMAP) satellite mission measures Earth’s radiation in the protected portion of the spectrum at 1.413 GHz (L-band) to retrieve geophysical quantities of the surface, such as soil moisture and the frozen/thawed state of the soil. The presence of radio-frequency interference (RFI) in this band is significant and impacts the quality of SMAP measurements. Knowing the location of the sources of RFI is important, because it can help to identify the source itself and also be used to develop strategies to mitigate its impact of the RFI on the data. This paper presents an algorithm that takes advantage of the viewing geometry of SMAP to locate sources of RFI. The results are validated using known locations of RFI sources and by comparison with the measurements of Soil Moisture and Ocean Salinity (SMOS) and Aquarius, two other satellite missions with L-band microwave radiometers operating in the protected band. Comparison with RFI of known location suggests that the algorithm is accurate to 1–2 km. The median distance between the locations reported by SMOS and this algorithm is 2.27 km. A study of the relationship between the localization error and the number of observations of RFI sources shows that the median localization error is about 2 km with 12 observations and about 1 km with 30 observations.

A CNN and LSTM-based approach to classifying transient radio frequency interference

Abstract Transient radio frequency interference (RFI) is detrimental to radio astronomy. It is typically broadband, intermittent and particularly difficult to classify by source. RFI of this type can be generated by devices like mechanical relays, fluorescent lights or AC machines. Such sources may be present in the vicinity of a radio telescope array, especially if other new instruments are under construction nearby. One mitigating approach is to deploy independent RFI monitoring stations at radio telescope arrays. Once the sources of RFI signals are identified, they may be removed or replaced where possible. For the first time in the open literature, we demonstrate an approach to classifying the sources of transient RFI (in time domain data) using deep learning techniques. Our proposed model includes a pre-trained CNN followed by a bidirectional LSTM layer. Applied to a previously obtained dataset of experimentally recorded transient RFI signals, our approach offers good results. It shows potential for development into a tool for identifying the sources of transient RFI signals recorded by RFI monitoring stations.

A Dictionary Approach to Identifying Transient RFI

AbstractAs radio telescopes become more sensitive, the damaging effects of radio frequency interference (RFI) become more apparent. One particular approach to mitigating RFI is to deploy independent, omnidirectional wideband monitoring systems at radio telescope arrays. Near such radio telescopes, detected RFI sources are often easily removed or replaced; the challenge lies in identifying them. Transient (impulsive) RFI is particularly difficult to identify. We propose a novel dictionary‐based approach to transient RFI identification. RFI events are treated as sequences of subevents, drawn from particular labeled classes. We demonstrate an automated method of extracting and labeling subevents using a data set of transient RFI. A dictionary of labels may be used in conjunction with hidden Markov models to identify the sources of RFI events reliably. We attain improved classification accuracy over traditional approaches such as support vector machines or a naïve k‐Nearest Neighbor classifier. Finally, we investigate why transient RFI is difficult to classify. We show that cluster separation in the principal components domain is influenced by the alternating current supply phase for certain sources.

A FPGA-based Fast Converging Digital Adaptive Filter for Real-time RFI Mitigation on Ground Based Radio Telescopes

Radio Frequency Interference (RFI) is a growing concern in the radio astronomy community. Single-dish telescopes are particularly susceptible to RFI. Several methods have been developed to cope with RF-polluted environments, based on flagging, excision, and real-time blanking, among others. All these methods produce some degree of data loss or require assumptions to be made on the astronomical signal. We report the development of a real-time, digital adaptive filter implemented on a Field Programmable Gate Array (FPGA) capable of processing 4096 spectral channels in a 1 GHz of instantaneous bandwidth. The filter is able to cancel a broad range of interference signals and quickly adapt to changes on the RFI source, minimizing the data loss without any assumption on the astronomical or interfering signal properties. The speed of convergence (for a decrease to a 1%) was measured to be 208.1 μs for a broadband noise-like RFI signal and 125.5 μs for a multiple-carrier RFI signal recorded at the FAST radio telescope.

RFI localization in synthetic aperture interferometric radiometers based on sparse Bayesian inference

ABSTRACTThe presence of radio frequency interference (RFI) sources emitting in the L-band, which is reserved for passive measurements by International Telecommunications Union (ITU) regulations, has seriously deteriorated the data quality of many brightness temperature (BT) snapshots in the Soil Moisture and Ocean Salinity (SMOS) project. In order to obviate the Gibbs-like contamination on the BT maps, one effective way is to locate the positions of RFI sources and switch them off. This article discusses a new method for RFI localization that is tailored to the scenario of synthetic aperture interferometric radiometry. The novel aspect lies in addressing the problem of RFI localization from a probabilistic viewpoint. By introducing the sparsity of RFI distribution in the spatial domain as a priori knowledge, we have employed the sparse Bayesian inference (SBI) strategy to estimate the locations of RFI sources. In addition, we have also tested the proposed method using numerical simulations and actual SMOS data. The results indicate that the proposed method has advantages in both accuracy and resolution of RFI source localization over the conventional direction-of-arrival (DOA) methods used in the beamforming technique.

Analysis of Radio Frequency Interference in Metop ASCAT Backscatter Measurements

The advanced scatterometer (ASCAT) is a radar system carried on board the ESA/EUMETSAT METOP series of satellites. It is designed for the purpose of retrieving wind field over oceans. It also provides information on surface soil moisture content and sea ice. Although ASCAT uses a linear frequency modulated pulse with a center frequency of 5.255 GHz (C-band), it is subject to radio frequency interference (RFI). This paper analyses seven years of ASCAT data and shows an increase of the number of noise outliers and an increase of the noise background level over specific land areas. This suggests that the outliers are not a natural occurrence, but are due to RFI from ground-based equipments. As regards the observed increase of the noise background level, it is not straightforward to associate possible RFI sources which could have caused it. However, since the ASCAT has a dynamic range of about 30 dB, the worse measured increase of 1 dB in the noise floor has almost no impact on performance, in particular, on soil moisture retrieval. In addition, the effect of the noise outliers on the estimate of the ASCAT receiver filter shape function used in the processing is also examined and is found to introduce errors of up to 0.4 dB. However, the occurrence of the noise outliers is generally very low, typically two out of 60 000 noise measurements per day, so the impact on the operational use of ASCAT data for wind vector retrieval is limited.

RFI Mitigation in Aperture Synthesis Radiometers Using a Modified CLEAN Algorithm

For aperture synthesis radiometers, sparse samplings on the $u$ - $v$ frequency plane cause undesirable sidelobes in the synthesized beam. Through these sidelobes, artificial sources emitting in the protected 1400–1427 MHz band contaminate the retrievals of the soil moisture and ocean salinity (SMOS) from MIRAS measurements. One effective way to correct the artificial interferences is to create a synthetic signal to compensate for the interference’s impact. Based on the similar idea, in this letter, we describe an algorithm to compensate for the interference’s impact by constructing an artificial signal as close as possible to the Gaussian beam. Numerical studies using synthetic and real SMOS data have been carried out to demonstrate that the proposed algorithm outperforms the classical CLEAN algorithm in correcting the impact of the extended radio frequency interference source.

Interference Localization for Satellite Navigation Systems

Global navigation satellite systems (GNSS) and, in particular, the global positioning system (GPS) have become ubiquitous in safety critical infrastructure. Vulnerability of GNSS to radio frequency interference (RFI) from either intentional (jamming) or unintentional sources is an ever growing concern. Hence, GNSS itself has become critical infrastructure which must be protected and its vulnerability to interference alleviated. As the RFI source is unknown a priori , passive localization systems are required; this adds an order of difficulty when compared with transmitter location systems with known and cooperative sources. The need for rapidly localizing the RFI leads to sensor network techniques which consist of spatially distributed sensor nodes (SNs). The localization systems typically use the received signal strength (RSS), source angle of arrival (AOA)/ direction of arrival (DOA), time difference of arrival (TDOA) or a combination of AOA(DOA)/TDOA or frequency difference of arrival (FDOA) measurements to estimate the RFI position. This paper provides an overview of existing systems from the literature and a comparison of these different interference geo-localization techniques.

Efficient and Robust RFI Extraction Via Sparse Recovery

This paper presents a simple adaptive framework for robust separation and extraction of multiple sources of radio-frequency interference (RFI) from raw ultra-wideband (UWB) radar signals in challenging bandwidth management environments. RFI sources pose critical challenges for UWB systems since 1) RFI often occupies a wide range of the radar’s operating frequency spectrum; 2) RFI might have significant power; and 3) RFI signals are difficult to predict and model due to the nonstationary nature as well as the complexity of various communication devices. Our proposed framework involves an initial RFI estimation step that operates directly on already contaminated radar signals to identify RFI-dominant frequency sub-bands. This vital prior information is then taken into account to construct an adaptive RFI dictionary with various sinusoidal patterns covering the aforementioned RFI-contaminated frequency spectrum. Finally, we employ a sparsity-driven optimization strategy to separate and then extract RFI from the received radar signals. Our method can be implemented as a denoising preprocessing stage for raw radar signals prior to image formation and other follow-up tasks such as target detection and classification. Recovery results from extensive simulated data sets as well as real-world signals collected by the U.S. Army Research Laboratory (ARL) UWB synthetic aperture radar (SAR) systems illustrate the robustness and effectiveness of our proposed framework.

Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation

The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over six years while the retrieval algorithms from Level 1 (L1) to Level 2 (L2) underwent significant evolutions as knowledge improved. Moreover, other approaches for retrieval at L2 over land were investigated while Level 3 (L3) and Level 4 (L4) were initiated. In this paper, these improvements were assessed by inter-comparisons of the current L2 (V620) against the previous version (V551) and new products (using neural networks referred to as SMOS-NN) and L3 (referred to as SMOS-L3). In addition, a global evaluation of different SMOS soil moisture (SM) products (SMOS-L2, SMOS-L3, and SMOS-NN) was performed comparing products with those of model simulations and other satellites. Finally, all products were evaluated against in situ measurements of soil moisture (SM). To achieve such a goal a set of metrics to evaluate different satellite products are suggested. The study demonstrated that the V620 shows a significant improvement (including those at L1 improving L2) with respect to the earlier version V551. Results also show that neural network based approaches can often yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved through the significant reduction of RFI sources in several areas of the world. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

An RFI Index to Quantify the Contamination of SMOS Data by Radio-Frequency Interference

The quality of science data provided by ESA soil moisture and ocean salinity (SMOS) satellite is degraded by the presence of artificial sources emitting in the protected part of the L-band, which is preserved for passive measurements by ITU regulation. These sources appear as high temperature points in SMOS brightness temperature products (e.g., Level 1C products), and may affect the retrievals of both SMOS (e.g., Level 2 products). In this contribution, a method is presented to quantify the impact of radio-frequency interference (RFI) on each SMOS snapshots, through the definition of an “RFI index” based on the number, position, and intensity of the RFI sources present in the snapshot. The main purpose of RFI indices is to provide the user of SMOS scientific data with information to ease the RFI filtering, thus achieving more accurate results. The comparison of RFI indices with the outputs of two different methods providing similar snapshot-wise information about RFI shows that the use of RFI indices reduces the probability of missed RFI detections, without increasing the risk for false alarms.

Geolocation of RFI sources with sub-kilometric accuracy from SMOS interferometric data

Abstract The SMOS mission is a European Space Agency project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. Although the L-band is a protected band, the data collected by SMOS are contaminated by radio frequency interferences (RFI) which degrade the performance of the mission. A precise location of the RFI emitters is required for switching off illegal transmissions or for fixing malfunctioning equipments. This work is concerned with the geolocation of such sources with a sub-kilometric accuracy from SMOS interferometric data themselves. Such a precise location has never been reached by any other published methods using only SMOS measurements.

Improved MUSIC-Based SMOS RFI Source Detection and Geolocation Algorithm

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has been providing L-band brightness temperature (BT) using its instrument, the Microwave Imaging Radiometer using Aperture Synthesis. In the measurements, the negative effect of radio frequency interference (RFI) is clearly present, deteriorating the quality of geophysical parameter retrieval. Detection and geolocation of RFI sources are essential to remove or at least mitigate the RFI impacts and ultimately improve the performance of parameter retrieval. This paper discusses a new approach to SMOS RFI source detection, based on the MUltiple SIgnal Classification (MUSIC) algorithm. Recently, the feasibility of MUSIC direction-of-arrival estimation has been shown for the RFI source detection of the synthetic aperture interferometric radiometer. This paper refines the MUSIC RFI source detection algorithm and tailors it to the SMOS scenario. To consolidate the RFI source detection procedure, several required steps are devised, including the rank estimation of the covariance matrix, local peak detection and thresholds, and multiple-snapshot processing. The developed method is tested using a number of SMOS visibility samples. In the test results, the MUSIC method shows an improvement on the accuracy and precision of the RFI source geolocation, compared with a simple detection method based on the local peaks of BT images. The MUSIC results especially outperform the SMOS BT image on the spatial resolution.

A Methodology to Determine Radio-Frequency Interference in AMSR2 Observations

A study to determine radio-frequency interference (RFI) in low-frequency passive microwave observations of the Advanced Microwave Scanning Radiometer-2 (AMSR2) is performed. RFI detection methods, such as the spectral difference method, have already been applied on microwave satellite sensors. However, these methods may result in false RFI detection, particularly in zones with extreme environmental conditions. To overcome this problem, this paper proposes an approach that uses the additional 7.3-GHz channel of the AMSR2 sensor in a new RFI detection method. This method uses calculated standard errors of estimate to detect RFI contamination in 6.9- and 7.3-GHz observations. It was found that 6.9-GHz observations are mainly contaminated in the USA, India, Japan, and parts of Europe. The 7.3-GHz observations are contaminated in South America, Ukraine, the Middle East, Southeast Asia, and Russia. The fact that these channels are not affected by RFI in exactly the same regions is useful for studies that prefer C-band brightness temperature observations (e.g., soil moisture retrieval algorithms). Therefore, a decision tree approach was set up to determine RFI and to select reliable brightness temperature observations in the lowest frequency free of any man-made contamination. The result is a reduction of the total contaminated pixels in the 6.9-GHz observations of 66% for horizontal observations and even 85% for vertical observations when 7.3 and 10.7 GHz are used. By linking RFI maps with civilization maps, this paper further shows that RFI sources at the C-band frequency are mainly located in urbanized areas.