Radio Frequency Interference Mitigation Research Articles

Deep, Broad Null Formation for Canceling Moving RFI in Radio Astronomical Arrays

Radio frequency interference (RFI) is rapidly becoming a major issue for many applications. It is especially problematic for radio astronomy, where signal-to-noise ratios (SNRs) are less than unity. Antenna array systems such as phased array feeds (PAFs) and interferometric imaging arrays are able to cancel RFI through adaptive projection-based spatial notch filtering techniques. Current methods for formulating these projection operators suffer from an unfortunate trade-off. They must sacrifice integration time when calculating the sample spatial correlation matrix in order to track RFI motion, but this consequently increases sample estimation error and reduces null depth. In this work, we propose a new way to process spatial correlation matrices to form a broad null that reliably cancels moving RFI without increasing sample estimation error due to insufficient integration. Additionally, when assisted by an RFI-tracking auxiliary antenna, this approach also reduces the total data rate coming out of a spatial correlator, thus making broad-null-based RFI cancelation more computationally efficient and practical for real-time active array-based RFI mitigation systems.

Implementing RFI Mitigation in Radio Science

This paper presents an overview of methods for mitigating radio frequency interference (RFI) in radio science data. The primary purpose of mitigation is to assist observatories to take useful data outside frequency bands allocated to the Science Services (Radio Astronomy Service (RAS) and Earth Exploration Service (EESS)): mitigation should not be needed within passive bands. Mitigation methods may be introduced at a variety of points within the data acquisition system in order to lessen the RFI intensity and to limit the damage it causes. These methods range from proactive methods to changing the local RFI environment by means of regulatory manners, to pre- and post-detection methods, to various pre-processing methods, and to methods applied at or post-processing.

Novel RFI Mitigation Methods in the Square Kilometre Array 1 Mid Correlator Beamformer

The Square Kilometre Array (SKA) Phase 1 Mid Correlator Beamformer (Mid.CBF) adopts two novel methods to mitigate radio frequency interference (RFI) at the various stages of its signal chains. First, the pioneering Sample Clock Frequency Offset (SCFO) sampling suppresses interference which leaks into individual ‘Frequency-Slice’ (FS) (sub-bands) in the cross-correlations. Second, the ‘Shift-Resample-Shift-Back’ method minimizes the addition of noise due to strong clustered RFI. Empirical studies conducted with simulation of the systems confirm that the proposed methods significantly reduce the impact of RFI on the output of the radio telescope.

Iridium Satellite Signals: A Case Study in Interference Characterization and Mitigation for Radio Astronomy Observations

Several post-detection approaches to the mitigation of radio-frequency interference (RFI) are compared by applying them to the strong RFI from the Iridium satellites. These provide estimates for the desired signal in the presence of RFI, by exploiting distinguishing characteristics of the RFI, such as its polarization, statistics, and periodicity. Our data are dynamic spectra with full Stokes parameters and 1[Formula: see text]ms time resolution. Moreover, since most man-made RFI is strongly polarized, we use the data to compare its unpolarized component with its Stokes I. This approach on its own reduces the RFI intensity by many tens of dBs. A comprehensive approach that also recognizes non-Gaussian statistics, and the time and frequency structure inherent in the RFI, permits exceedingly effective post-detection excision provided full Stokes intensity data are available.

Design of GPS antijamming algorithm using adaptive array antenna to mitigate the noise and interference

This paper describes the Matlab based simulation of radio frequency interference monitoring and mitigation techniques using adaptive array antenna and null steering algorithm. The proposed research is described to show the design and implementation of the GPS transmitter and receiver system for real time navigation, location based services and last but not least tracking applications. Two interferences arriving at 35° and 55° with power greater than seven times of original GPS signals arriving at 45°. Least mean square (LMS) and recursive least square (RLS) algorithms. The proposed system has been evaluated by conducting experiments by using the simulation tool for AntiJammer. The proposed model is useful for developing the smart health care application. Results showed better performance of RLS over LMS to mitigate the effect of interference as well as noise with a higher signal to noise ratio. This system will be useful for enhancing the communication effectiveness of smarthealth care systems.

Characteristics of L-band radio frequency interference detected via the soil moisture active passive radiometer in China and its offshore areas

Abstract Detection and mitigation of radio frequency interference (RFI) is one of the most crucial issues in microwave remote sensing measurements. This study analyzes the brightness temperature and RFI acquired by the soil moisture active passive (SMAP) satellite L-band radiometer over China and its offshore areas, revealing that the RFI distribution is uneven. In China, RFI is mainly concentrated in the urban agglomerations and their surrounding areas. Further, RFI and population density show strong positive correlation. A comparison of the radiometer antenna brightness temperature data and the data after RFI detection and mitigation shows that the volume of the detected RFI is ∼1.4 K and the standard deviation is 6.5. Under the influence of RFI, different bands and polarizations in the study area did not show significant differences.

RFI Mitigation for UWB Radar Via Hyperparameter-Free Sparse SPICE Methods

Radio frequency interference (RFI) causes serious problems to ultrawideband (UWB) radar operations due to severely degrading radar imaging capability and target detection performance. This paper formulates proper data models and proposes novel methods for effective RFI mitigation. We first apply the single-snapshot Sparse Iterative Covariance-based Estimation (SPICE) algorithm to data from each pulse repetition interval for RFI mitigation and discuss the connection of SPICE to the $l_{1}$ -penalized least absolute deviation ( $l_{1}$ -PLAD) approach. Then, we devise a modified group SPICE algorithm and we prove that it is equivalent to a special case of the $l_{1,2}$ -PLAD method. The modified group SPICE algorithm can be applied to data from a coherent processing interval for effective RFI mitigation. Both the single-snapshot SPICE and the modified group SPICE methods simultaneously exploit the sparsity properties of both RFI spectrum and UWB radar target echoes. Unlike the existing sparsity-based RFI suppression methods, such as the robust principal component analysis algorithm, the proposed methods are hyperparameter-free and therefore easier to use in practical applications. Furthermore, the fast implementation of the SPICE methods is considered by exploiting the special structures of both single-snapshot and multiple-snapshot covariance matrices. Finally, the results obtained from applying the SPICE methods to simulated data as well as measured data collected by the U.S. Army Research Laboratory synthetic aperture radar system are presented to demonstrate the effectiveness of the proposed methods.

The High Time Resolution Universe survey – XIV. Discovery of 23 pulsars through GPU-accelerated reprocessing

We have performed a new search for radio pulsars in archival data of the intermediate and high Galactic latitude parts of the Southern High Time Resolution Universe pulsar survey. This is the first time the entire dataset has been searched for binary pulsars, an achievement enabled by GPU-accelerated dedispersion and periodicity search codes nearly 50 times faster than the previously used pipeline. Candidate selection was handled entirely by a Machine Learning algorithm, allowing for the assessment of 17.6 million candidates in a few person-days. We have also introduced an outlier detection algorithm for efficient radio-frequency interference (RFI) mitigation on folded data, a new approach that enabled the discovery of pulsars previously masked by RFI. We discuss implications for future searches, particularly the importance of expanding work on RFI mitigation to improve survey completeness. In total we discovered 23 previously unknown sources, including 6 millisecond pulsars and at least 4 pulsars in binary systems. We also found an elusive but credible redback candidate that we have yet to confirm.

Radio Frequency Interference Mitigation for High-Frequency Surface Wave Radar

Radio frequency interference (RFI) is a common source of interference for high-frequency surface wave radar (HFSWR) since the HF band is shared among many radio services. The existence of RFI greatly degrades the detection performance of HFSWR. On the basis of a detailed analysis of the characteristics of RFI, a new RFI mitigation method based on joint fractional Fourier transform (FRFT) and complex empirical mode decomposition (CEMD) is proposed in this letter. In this method, the optimum transform order of the FRFT is obtained through a two-level peak search. Subsequently, echoes that contain RFI are transformed into the fractional Fourier domain and are then decomposed into a number of intrinsic mode functions (IMFs) via CEMD. The RFI is mitigated by implementing detection and localization in each IMF. The reconstructed signal is then transformed into the time domain via an inverse FRFT, and the mitigated signal is finally obtained. Processing results using experimental data indicate that the proposed method can effectively suppress RFI without losing echoes.

Mitigation of RFI Main Lobes in SMOS Snapshots by Bandpass Filtering

Since the beginning of the soil moisture and ocean salinity mission, the pervading presence of radio frequency interferences (RFI) has been one of the most problematic issues. The effect of an RFI is not just a hot spot but also six tails along the three main axes, and the general presence of ripples which degrade the quality of L1 brightness temperature snapshots. The standard mitigation technique is to apply an apodization (Blackman), but such a low-pass filter leaves traces of the tails and spreads the signal of the main lobes. New RFI mitigation techniques, such as nodal sampling, are very effective in reducing the impact of tails and ripples, but in some cases they lead to the spread of the RFI main lobe, with a significant loss of data on the affected area. In this letter, we propose a new technique to reduce their spread by an adaptive thresholding on a bandpass filtered version of the snapshot, with a significant recovery of data.

Estimation and Mitigation of Time-Variant RFI in Low-Frequency Ultra-Wideband Radar

The work presented in this letter focuses on the time-variant radio frequency interference (RFI) issue in the low-frequency ultra-wideband (UWB) radar. Different from many previous studies, we first analyze the characteristics of RFIs and scattered echoes in the slow-time dimension and take advantage of overlapped short-time Fourier transform to adapt to the time-variant RFIs and update the frequency Doppler spectrum. Then, in the frequency Doppler spectrum, we adopt the minimum statistic combined with 1-D cell-averaging constant false alarm rate to estimate and separate the RFI power spectrum from the scattered echoes based on their differences. Finally, to mitigate the estimated RFIs, a suboptimal filter controlled by the defined entropy of range profiles after math filtering is designed. Employing a UWB radar, different experiments were conducted, and results verify the proposed method.

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.

Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis

The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer raw data is introduced. This procedure includes a thorough explanation of the calibration and filtering including a refined radio frequency interference (RFI) mitigation approach. This new mitigation approach not only performs better than conventional “normality” tests (kurtosis and skewness) but also allows for the quantification of measurement uncertainty introduced by non-thermal noise contributions. The brightness temperatures of natural snow covered areas and areas with a reflector beneath the snow are simulated for varying amounts of snow liquid water content distributed across the snow profile. Both measured and simulated brightness temperatures emanating from natural snow covered areas and areas with a reflector beneath the snow reveal noticeable sensitivity with respect to snow liquid water. This indicates the possibility of estimating snow liquid water using L-band radiometry. It is also shown that distinct daily increases in brightness temperatures measured over the areas with the reflector placed on the ground indicate the onset of the snow melting season, also known as “early-spring snow”.

Characterization and Mitigation of Radio Frequency Interference in PolSAR Data

AbstractPolarimetric synthetic aperture radar (PolSAR) is a very important instrument for active remote sensing. However, it is common to find that PolSAR echoes are often contaminated by incoherent electromagnetic interference, which is referred to as radio frequency interference (RFI). The analysis of RFI signatures and its influence on PolSAR data seems to be lacking in existing literatures, especially for PolSAR post products, such as the polarimetric decomposition parameters and clustering result. The goal of this paper is to reveal the link between RFI and polarization, as well as to analyze the impact of interference on PolSAR image and its post products. Qualitative and quantitative analyses of the adverse impact of RFI on the real measured NASA/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar data set are illustrated from two perspectives, that is, evaluation of imaging quality and interpretation of scattering mechanisms. The point target response and effective number of looks are evaluated for assessing the distortion to focusing quality. Further, we discussed the characteristics of ultra wideband RFI and proposed a mitigation method using nonnegative matrix factorization along azimuth direction. The experimental results indicate the effectiveness of the proposed method.

Ultra-low-loss high-pass filter with air-core short-circuit coaxial cable

This paper presents a novel ultra-low-loss high-pass filter with 3 dB cutoff frequency of 890 MHz for astronomical receiver radio frequency interference mitigation application. The filter consists of three series capacitors, four shunt inductors, and microstrip circuit board. Specially, 4 shunt inductors are actualized by fabricating air-core short-circuit coaxial cable inductors in the filter box body, and the quality factor of these cable inductors is up to 762.2 and 1046, respectively, at 1.4 GHz. In the expected passband of 1.1–1.9 GHz, S parameters measurement show that insertion loss is lower than 0.16 dB and two-port return loss is larger than 20 dB; noise measurement show that filter noise temperature is lower than 8 at 300 K ambient temperature. Noise sources of this filter are analyzed by simulation and measurement.

L-band vegetation optical depth and effective scattering albedo estimation from SMAP

Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. Attenuation, as represented by vegetation optical depth (VOD), is a potentially useful ecological indicator. The NASA Soil Moisture Active Passive (SMAP) mission carries significant potential for VOD estimates because of its radio frequency interference mitigation efforts and because the L-band signal penetrates deeper into the vegetation canopy than the higher frequency bands used for many previous VOD retrievals. In this study, we apply the multi-temporal dual-channel retrieval algorithm (MT-DCA) to derive global VOD, soil moisture, and effective scattering albedo estimates from SMAP Backus-Gilbert enhanced brightness temperatures posted on a 9km grid and with three day revisit time. SMAP VOD values from the MT-DCA follow expected global distributions and are shown to be highly correlated with canopy height. They are also broadly similar in magnitude (though not always in seasonal amplitude) to European Space Agency Soil Moisture and Ocean Salinity (SMOS) VOD. The SMOS VOD values are based on angular brightness temperature information while the SMAP measurements are at a constant incidence angle, requiring an alternate approach to VOD retrieval presented in this study.Globally, albedo values tend to be high over regions with heterogeneous land cover types. The estimated effective scattering albedo values are generally higher than those used in previous soil moisture estimation algorithms and linked to biome classifications. MT-DCA retrievals of soil moisture show only small random differences with soil moisture retrievals from the Baseline SMAP algorithm, which uses a prior estimate of VOD based on land cover and optical data. However, significant biases exist between the two datasets. The soil moisture biases follow the pattern of differences between the MT-DCA retrieved and Baseline-assigned VOD values.

Performance Assessment of Time–Frequency RFI Mitigation Techniques in Microwave Radiometry

Radio–frequency interference (RFI) signals are a well-known threat for microwave radiometry (MWR) applications. In order to alleviate this problem, different approaches for RFI detection and mitigation are currently under development. Since RFI signals are man made, they tend to have their power more concentrated in the time–frequency (TF) space as compared to naturally emitted noise. The aim of this paper is to perform an assessment of different TF RFI mitigation techniques in terms of probability of detection, resolution loss (RL), and mitigation performance. In this assessment, six different kinds of RFI signals have been considered: a glitch, a burst of pulses, a wide-band chirp, a narrow-band chirp, a continuous wave, and a wide-band modulation. The results show that the best performance occurs when the transform basis has a similar shape as compared to the RFI signal. For the best case performance, the maximum residual RFI temperature is 14.8 K, and the worst RL is 8.4%. Moreover, the multiresolution Fourier transform technique appears as a good tradeoff solution among all other techniques since it can mitigate all RFI signals under evaluation with a maximum residual RFI temperature of 21 K, and a worst RL of 26.3%. Although the obtained results are still far from an acceptable bias $ 1 K for MWR applications, there is still work to do in a combined test using the information gathered simultaneously by all mitigation techniques, which could improve the overall performance of RFI mitigation.

Performance Analysis of Circular Antenna Array for Microwave Interferometric Radiometers

The microwave interferometric radiometer (MIR) is a passive sensor using synthetic aperture technique for microwave remote sensing. It uses a thinned antenna array to replace the traditional large real aperture antenna, so as to reduce the antenna size, weight, and manufacturing difficulty, and further increase the spatial resolution of the instrument. The antenna array configuration is the first concern for the MIR application. Various array configurations have been proposed. Among them the arrays with hexagonal sampling grid such as the Y-shaped and hexagonal arrays have drawn more attention because of the higher sampling efficiency. However, they do not have the best performance in radiometric sensitivity and radio frequency interference (RFI) mitigation. This paper presents the detailed analysis of the circular array performance and compares it with the Y-shaped and hexagonal arrays. Based on the early developed theory about the radiometric resolution, two new factors named as sampling sensitivity factor and uncertainty factor are first introduced and defined in this paper, which can be used to quantitatively characterize the array performance. The main beam efficiency of the MIR is also redefined to prevent it being uncomfortably greater than unity but still keep a physical significance. Numerical simulations with analysis are finally implemented to verify the theoretical results and also to demonstrate the superiority of the circular array in terms of radiometric sensitivity and RFI mitigation.

Radio Frequency Interference Mitigation in High-Frequency Surface Wave Radar Based on CEMD

Radio frequency interference (RFI) is a common interference source in high-frequency surface wave (HFSW) radar. Its existence degrades the performance of HFSW radar greatly and makes it necessary to find an effective method to mitigate the interferences. There are two kinds of RFI in the experimental data. One is transient RFI, which is usually suppressed by temporal processing. The other is nontransient RFI, which is suppressed by adaptive beamforming methods. However, the temporal processing techniques suffer performance loss in nontransient cases, whereas the adaptive beamforming methods need spatially structuring, which is difficult to meet within the coherent integration time (CIT) of a few minutes. The fact that the experimental data are usually interfered by two kinds of RFI over a CIT motivates us to find a unified method for interference mitigation. In this letter, a new method based on complex empirical mode decomposition (CEMD) is proposed. CEMD is a local decomposition algorithm that can decompose the echoes and the RFI including transient and nontransient RFI into different intrinsic mode functions (IMFs). Then, the IMFs that correspond to RFI are processed. Experimental results indicate that the proposed method can effectively mitigate both kinds of RFI, and improve the signal-to-noise ratio without losing echoes.

Radio frequency interference mitigation using deep convolutional neural networks

Abstract We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net , that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE & SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK ’s SumThreshold implementation. We publish our U-Net software package on GitHub under GPLv3 license.