Narrowband Radio Frequency Interference Research Articles

Radio Frequency Interference Mitigation in Synthetic Aperture Radar Data Based on Instantaneous Spectrum Forward Consecutive Mean Excision

Radio frequency interference (RFI) poses major threats to synthetic aperture radar (SAR) systems. Due to the suppression of useful target signals via high-power RFI, the SAR imaging quality is severely degraded. Nevertheless, existing studies on RFI mitigation mainly focus on narrowband filtering, while wideband RFI mitigation methods are relatively lacking and perform non-robustly. In this paper, an RFI mitigation scheme is proposed based on instantaneous spectrum forward consecutive mean excision (FCME), which is suitable for both narrowband and wideband RFI mitigation. The SAR echo signal is first transformed into a time–frequency (TF) domain through a short-time Fourier transform (STFT). On this basis, the instantaneous spectra polluted via RFI are detected via a kurtosis-based statistical test and then filtered via FCME to achieve RFI mitigation. Finally, connected component analysis is applied as a safety measure so as to avoid the unnecessary loss of useful target signal. The combination of FCME and connected component analysis enables the proposed method to thoroughly filter out RFI while retaining more useful target signals compared with other competing methods. The experimental results on real SAR raw data validate the effectiveness of the proposed method.

A Search for Extraterrestrial Technosignatures in Archival FAST Survey Data Using a New Procedure

The search for extraterrestrial intelligence (SETI) commensal surveys aim to scan the sky to find possible technosignatures from an extraterrestrial intelligence (ETI). The mitigation of radio frequency interference (RFI) is an important step, especially for the most sensitive Five-hundred-meter Aperture Spherical radio Telescope (FAST), which can detect more weak RFI. In this paper, we propose several new techniques for RFI mitigation and use our procedure to search for ETI signals from the archival data of FAST’s first SETI commensal survey. We detect the persistent narrowband RFI by setting a threshold of the signals’ sky separation and detect the drifting RFI (and potentially other types of RFI) using the Hough transform. We also use the clustering algorithms to remove more RFI and select candidates. The results of our procedure are compared to the earlier work on the same FAST data. We find that our methods, though relatively simpler in computation, remove more RFI (99.9912% compared to 99.9063% in the earlier work) but preserve the simulated ETI signals, except for those (5.1%) severely affected by the RFI. We also report more interesting candidate signals, about a dozen of which are new candidates that were not previously reported. In addition, we find that the proposed Hough transform method, with suitable parameters, also has the potential to remove the broadband RFI. We conclude that our methods can effectively remove the vast majority of the RFI while preserving and finding the candidate signals that we are interested in.

Study on the environmental effect on human being detection using the UWB bio-radar

Objective: Non-contact penetrating detection and sensing of human beings (vital signs) through nonmetallic obstacles (ruins, wall, and smog) using the ultra-wideband (UWB) bio-radar plays a significant role in various post-disaster rescue operations in national public security events, like earthquake, building collapse, and factory explosion. In practical application scenarios, the narrowband radio frequency interference (RFI) and surrounding movement interference (SMI) are the two most common and major types of environmental interference (EI), which would cause serious effects on the penetrating detection performance of the UWB bio-radar.Methods: Therefore, through establishing a quantitative and controllable experiment system and combining a proposed quantitative evaluation method, this paper quantitatively investigates and evaluates the influencing characteristics and laws of these two interferences on human vital sign detection using the UWB bio-radar. In the quantitative experiments, two key environmental spatial parameters (interference distance and angle) of interference sources and two kinds of system parameters (main lobe width and time window) of the UWB bio-radar are considered.Results: Numerous experiments at different interference positions and corresponding statistical results demonstrated that both the RFI and SMI have a high negative correlation with the interference distance. Meanwhile, the SMI, which is highly related to the interference angle, could be screened or isolated by the detecting time window and main lobe boundary of UWB bio-radar, but the RFI could not be detected.Discussion: This study shows potential in providing transcendental knowledge and guidance for special EI suppression study, and even for the future design, manufacture, and rational use of the UWB bio-radar, facilitating improvement in the practical performance and effectiveness of the UWB bio-radar search and rescue system.

Multistage FPGA Least Mean Squares Filters Suppressing Multiple Narrowband Radio-Frequency Interferences in the Radio Detection of Cosmic Rays

The article presents the FPGA implementation of 256- and 768-stage Least Mean Squares filters with rectangular, Hamming and Kaiser windows to suppress at least 10 narrow-bands RFIs. The analysis shows extremely high efficiency Radio Frequency Interference (RFI) elimination. 9-bit real cosmic ray data from the Auger Engineering Radio Array (AERA) was used as patterns and mixed with 14-bit multiple narrow-band RFIs. They were hidden deep within RFI’s background. After the cleaning process, the patterns were brought out and are practically without contamination.

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.

Ship Detection in Spaceborne SAR Images under Radio Interference Environment Based on CFAR

Spaceborne synthetic aperture radar (SAR) can be easily interfered with by narrowband radio frequency interference (RFI) from ground radiation sources, causing significant degradation of image quality. In the application of SAR ship detection, the radio interference will raise the detection threshold of a constant false alarm rate (CFAR) detector, and consequently results in the degradation of detection performance. In order to solve this problem, we propose a ship-detection method for SAR under a narrowband RFI environment. The proposed method is mainly divided into five steps: (1) transform the input SAR image with narrowband RFI into 2-D frequency domain by fast Fourier transform (FFT); (2) use CFAR detector to detect RFI in 2-D frequency domain; (3) suppress RFI data points using adaptively weighting in the 2-D frequency domain; (4) transform the RFI suppressed 2-D spectrum into the image domain via inverse FFT; (5) apply CFAR detector for ship detection. Simulation and real data experiments show that the proposed method can effectively detect ships from SAR images with ocean background even if there exists serious RFI.

Methodology for Blockwise Behavioral Modeling of ICs for Narrow-Band RF Interference Injection

A behavioral modeling methodology based on echo state networks for time-domain simulations of the conducted immunity of integrated circuits is proposed. The methodology is applied to a buffered voltage reference integrated circuit that is subjected to dc functional signals with superimposed radio-frequency (RF) interference signals. Behavioral models of three analog subcircuits of the buffered voltage reference are built that capture the RF behavior of the voltages and currents observed in the time-domain simulations of the narrow-band RF interference injection. The behavioral models are interchangeable with the transistor-level subcircuits and they ensure the correct nonlinear impedance toward the connected transistor-level circuits. The conditions for the stability of the resulting top-level circuit with different combinations of behavioral model and transistor-level subcircuits are derived, and the accuracy of the presented behavioral models in the circuit simulator is evaluated.

A modified EMD method for detecting and mitigating narrow-band RFI in SAR data

ABSTRACT The empirical mode decomposition (EMD) based method for mitigating radio frequency interference (RFI) has the capability of removing narrow-band RFI and the advantage of easy-to-implement. The signals are decomposed by EMD into a finite number of intrinsic mode functions (IMFs) in a pulse-by-pulse manner, and then the IMFs corresponding to the RFI are subtracted to achieve interference mitigation. However, useful signals will be lost by using this method, which reduces the synthetic aperture radar (SAR) imaging quality. In this letter, a modified EMD method is proposed to tackle this problem. Firstly, the interferences of the echoes are detected through the relative energy ratio. Secondly, echoes containing RFI are decomposed into many IMFs via EMD. These IMFs are transformed into time-frequency (TF) domain via short-time Fourier transform (STFT), and then IMFs corresponding to RFI are screened out via the robust bisecting k-means algorithm. Finally, the RFIs in TF domain are located with the robust OTSU algorithm and mitigated by a notch operation. Experimental results of real SAR data with simulated RFIs demonstrate the effectiveness and superiority of the proposed method.

RFI Suppression for SAR via a Dictionary-Based Nonconvex Low-Rank Minimization Framework and Its Adaptive Implementation

Synthetic aperture radar (SAR) frequently suffers from radio frequency interference (RFI) due to the simultaneous presence of numerous wireless communication signals. Recently, the narrowband RFI is found to possess the low-rank property benefiting from stable frequency occupancy, hence the reconsideration of RFI suppression as a joint sparse and low-rank optimization problem. The existing methods either use the non-sparse useful signal itself as the sparse regularizer, or employ the nuclear norm to approximate the rank function, which punishes all singular values with the same penalty via singular value thresholding (SVT), resulting in the improper punishment problem. Hence, both are consequentially subject to performance limitation. In this paper, a novel dictionary-based nonconvex low-rank minimization (DNLRM) optimization framework is proposed for RFI suppression, which concurrently considers the improvements for both the sparse regularizer and the low-rank regularizer. For the former, an over-completed dictionary is constructed, for which the sparse coefficient acts as the sparse regularizer. For the latter, the rank function is more accurately approximated by innovatively introducing the nonconvex function, for which the supergradient is synchronously used to generate the weighted penalty, thus solving the improper punishment problem. The derivation of the closed-form solution and the convergence analysis are described in detail. Additionally, the adaptive selection scheme for the model parameter is uniquely proposed for further ensuring the practicality of the DNLRM framework. The superiority of the proposed method is demonstrated via not only the RFI-free real SAR data combined with the measured RFI, but the RFI-contaminated real SAR data.

An Efficient Radio Frequency Interference Mitigation Algorithm in Real Synthetic Aperture Radar Data

As a wideband radar system, a synthetic aperture radar (SAR) may conflict with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, termed as radio frequency interference (RFI), may severely interfere SAR systems from generating a high-resolution image. Numerous previous researches focused on the RFI suppression problem, among which the semiparametric methods have been verified to have the state-of-the-art performance. However, most of the semiparametric methods are computationally expensive and can hardly be used on wide-swath SAR imaging processing. In this article, an efficient semiparametric algorithm is proposed to suppress RFIs via alternating projections. It has comparable performance as the other methods but significantly improves the computational efficiency a lot. It is able to remove both narrowband and wideband RFIs and can be used directly on the Level-1 SAR data. Finally, multiple real SAR data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

CAPTURE: a continuum imaging pipeline for the uGMRT

We present the first fully automated pipeline for making images from the interferometric data obtained from the upgraded Giant Metrewave Radio Telescope (uGMRT) called CAsa Pipeline-cum-Toolkit for Upgraded Giant Metrewave Radio Telescope data REduction - CAPTURE. It is a python program that uses tasks from the NRAO Common Astronomy Software Applications (CASA) to perform the steps of flagging of bad data, calibration, imaging and self-calibration. The salient features of the pipeline are: i) a fully automatic mode to go from the raw data to a self-calibrated continuum image, ii) specialized flagging strategies for short and long baselines that ensure minimal loss of extended structure, iii) flagging of persistent narrow band radio frequency interference (RFI), iv) flexibility for the user to configure the pipeline for step-by-step analysis or special cases and v) analysis of data from the legacy GMRT. CAPTURE is available publicly on github (https://github.com/ruta-k/uGMRT-pipeline, release v1.0.0). The primary beam correction for the uGMRT images produced with CAPTURE is made separately available at https://github.com/ruta-k/uGMRTprimarybeam. We show examples of using CAPTURE on uGMRT and legacy GMRT data. In principle, CAPTURE can be tailored for use with radio interferometric data from other telescopes.

RFI Suppression Based on Linear Prediction in Synthetic Aperture Radar Data

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

Mismatched filter bank design for radio frequency interference suppression in multiple-input multiple-output over-the-horizon radar

Radio frequency interference (RFI) is a major problem in Multiple-Input Multiple-Output Over-the-Horizon Radar (MIMOOTHR). Selecting working frequencies and clean bandwidth in the complex electromagnetic environment is challenging using traditional frequency selection methods. We note that MIMOOTHR usually transmits Time-Staggered Linear Frequency Modulated Continuous Wave (TS-LFMCW) waveforms, so the target echo that occurs before matched filtering is a broadband (about tens of KHz) signal, while the RFI after frequency selection is usually a narrowband (about a few KHz) signal. To suppress the narrowband RFI and complete the waveform diversity process, the mismatched filter bank is designed to replace the original matched filter bank without changing the TS-LFMCW waveforms. First, we establish the mismatched filtering optimization problem for MIMOOTHR. We employ the output signal-to-interference-plus-noise ratio maximization criterion of adaptive RFI suppression and two constraints for adjustable target signal attenuation and TS-LFMCW waveforms diversity performance. Then, two analytic solutions to this constrained optimization problem are derived to guarantee the computational efficiency of the mismatched filtering. The proposed mismatched filtering methods can detect the target successfully in the presence of narrowband RFI. The RFI suppression performance, the waveform diversity performance and computational efficiency of the two mismatched filter banks are analysed using numerical results.

Efficient Narrowband RFI Mitigation Algorithms for SAR Systems With Reweighted Tensor Structures

Radio-frequency systems, such as TV and cellular networks, severely interfere with synthetic aperture radar (SAR) systems. Narrowband radio-frequency interference (RFI) has a special low-rank property in the received signal matrix, because it performs like a sinusoid with nearly invariant frequency as the slow time proceeds. Exploiting this special property, in this paper, we divide the received signal matrix into several small matrices, in each of which the RFI is also low rank. Without losing the connection between these small matrices, we stack them into a three-mode tensor to separate the low-rank RFI tensor and recover the informative signal tensor. Previous studies employed the nuclear norm to regularize the low-rank RFI, which is not a good choice. Hence, we propose two reweighted algorithms, the reweighted tensor nuclear norm (RTNN) and the reweighted tensor Frobenius norm (RTFN) algorithms, to approximate the rank function in a tensor and accurately extract the low-rank RFI tensor from the received signal tensor. As a result, the introduction of the tensor structure dramatically decreases the computational cost. Furthermore, the reweighted scheme helps suppressing the RFI and recovering the useful signal with excellent performance. Finally, real SAR data with measured RFI is employed to demonstrate the effectiveness of the proposed methods for RFI mitigation.

Least Mean Squares Filters Suppressing the Radio-Frequency Interference in AERA Cosmic Ray Radio Detection

The production of radio waves from extensive air showers (EASs), initiated by ultra-high-energy cosmic rays, has been attributed to geomagnetic emission and charge excess processes. These days, the radio detection technique is used in many experiments aimed at studying EAS. One of them is the Auger Engineering Radio Array (AERA), located within the Pierre Auger Observatory. The frequency band observed by AERA radio stations is 30–80 MHz. The investigated frequency range is often very contaminated by human-made and narrowband radio-frequency interference (RFI). Suppression of these contaminations is crucial to lowering the rate of spurious triggers. An adaptive filter based on the least mean squares (LMS) algorithm can be an alternative one for currently used infinite impulse response (IIR)-notch nonadaptive filters. Measurements show that the LMS filter is very efficient in suppressing RFI and only slightly distorts radio signals. This article presents the 32-stage filters based on a transposed finite impulse response (FIR) filter implemented into cost-effective CycloneIV and CycloneV Altera field-programmable gate arrays (FPGAs) with a sufficient safety margin of the timing performance for the global clock above 200 MHz to obey the Nyquist requirement.

Reweighted Nuclear Norm and Reweighted Frobenius Norm Minimizations for Narrowband RFI Suppression on SAR System

Synthetic aperture radar (SAR), as a wideband radar system, is subject to interference by radio frequency systems, such as radio, TV, and cellular networks. Since the narrowband radio frequency interference (RFI) has a stable frequency in a snapshot sequence, it has a low-rank property that can be used to substract RFI from the received signal. The nuclear norm is a common convex relaxation to constrain the rank, but it is optimized by the singular value thresholding (SVT) algorithm, which uses a single threshold to treat all singular values and greatly over-punishes large singular values. Hence, in this paper, we propose two methods, the reweighted nuclear norm (RNN) algorithm and the reweighted Frobenius norm (RFN) algorithm, to separate the RFI and the useful signal. The RNN and RFN minimization problems are the approximations of the real rank function, which can protect large singular values and restrict the rank. As a result, the RFI is accurately extracted and the useful signal is successfully protected. Also, we strictly derive the closed-form solutions of the RNN and RFN minimization problems for complex radar signals, and we also employ downsampling to extract the mainband of the signal spectrum to speed up the convergence. Real SAR data is applied to demonstrate the effectiveness of the proposed methods for RFI suppression.

Fast Narrowband RFI Suppression Algorithms for SAR Systems via Matrix-Factorization Techniques

A synthetic aperture radar (SAR) system is severely affected by radio frequency systems, such as TV and cellular networks. Previous studies showed that narrowband radio frequency interference (RFI) is low rank and used the nuclear norm as a low-rank regularization to extract the RFI from the received signal. However, the nuclear norm is not an appropriate approximation of the true rank function. Hence, in this paper, the reweighted matrix-factorization (RMF) algorithm and the matrix-factorization decomposition (MFD) algorithm are proposed to suppress narrowband RFI for SAR systems, where the RMF algorithm uses the reweighted scheme to approximate the rank function, while the MFD algorithm restrains the upper bound of the rank as a prior condition. Moreover, the introduction of the MF scheme dramatically decreases the computational complexity and efficiently suppresses RFI. In addition, we further show that the sparse regularization of the useful signal (i.e., the useful SAR echo) not only protects the strong scatterers of the useful signal but also avoids low-rank overfitting. We employ the real SAR signals of both the sparse scene and the nonsparse scene with the measured RFI to verify the effectiveness of the proposed methods, and the proposed methods outperform the other methods for RFI suppression.

Narrowband RFI Suppression for SAR System via Efficient Parameter-Free Decomposition Algorithm

Synthetic aperture radar (SAR), as a wideband radar system, is easy to be interfered by radio frequency systems, such as the radio, television, and cellular works. Since the narrowband radio frequency interference (RFI) has a relatively fixed frequency during the synthetic aperture time, it is removed as a low-rank term of the received signal in recent research. In this paper, we employ a novel “low-rank + sparse” decomposition model to extract the low-rank RFI and protect the strong scatterers of a useful signal, which is explicit and more efficient than the previous augmented Lagrange function model. Because the radar signal is complex, we exploit soft thresholding instead of hard thresholding in the Go Decomposition algorithm, which is defined as the revised traditional decomposition (RTD) method. Soft thresholding can recover the phase term correctly for a further focused image. Both the previous augmented Lagrange method and the proposed RTD method need to search the values of user parameters with high computational complexity. In order to eliminate the bother of tuning user parameters, a parameter-free decomposition (PFD) method is proposed to adaptively estimate the user parameters. Also, by considering the property of the useful signal, the PFD method protects the useful signal with adaptive thresholds for each snapshot. It has a better performance for RFI suppression, but costs slightly more computational time compared with the RTD method. The real SAR data and the measured RFI are provided to demonstrate the correctness of the proposed methods.

Narrowband RFI Suppression for SAR System via Fast Implementation of Joint Sparsity and Low-Rank Property

The synthetic aperture radar (SAR), as a wideband radar system, operates over a large frequency band ranging from the low very high frequency to millimeter waves. It often overlaps in frequency with other radio-frequency systems including radio, television, and cellular networks. Therefore, radio-frequency interference (RFI) suppression is the severe test for SAR systems. Recently, some methods are proposed to suppress the RFI based on the sparse recovery in range-frequency domain and low-rank extraction in the azimuth dimension. However, all the previous methods exploit one property of the RFI, which may leave the room for performance improvement. Hence, in this paper, we propose three methods to jointly exploit the sparsity and low-rank property of RFI. We first include the sparse term and low-rank term of the RFI in the objective function to separate the RFI and the useful signal, which is defined as the joint sparsity and low-rank property method. It has better performance but heavier computational burden than the algorithm employing only the low-rank property. Then, we use row-sparse (RS) concept in lieu of the two properties, since the narrowband RFI has relatively stable frequencies during the synthetic aperture time. The RS method avoids the low-rank optimization and dramatically decrease the computational burden. Also for the real radar system, the sampling frequency is commonly larger than the frequency bandwidth. Therefore, there are some redundant data in the received signal. We exploit both the downsampling operation and the RS concept to speed up the convergence, which is called the fast row-sparse (FRS) method. The FRS method can further eliminate the out-of-mainband RFI. The real SAR data are provided to demonstrate the effectiveness of the three proposed methods.

Implementing and Characterizing Real-Time Broadband RFI Excision System for the GMRT Wideband Backend

ABSTRACTThe Giant Metrewave Radio Telescope (GMRT) is being upgraded to increase the receiver sensitivity. This makes the receiver more susceptible to man-made Radio Frequency Interference (RFI). To improve the receiver performance in the presence of RFI, real-time RFI excision (filtering) is incorporated in the GMRT wideband backend (GWB). The RFI filtering system is implemented on FPGA (Field Programmable Gate Array) and CPU (Central Processing Unit)–GPU (Graphics Processing Unit) platforms to detect and remove broadband and narrowband RFI, respectively. The RFI is detected using a threshold-based technique where the threshold is computed using Median Absolute Deviation (MAD) estimator. The filtering is carried out by replacing the RFI samples by either noise samples or constant value or threshold. This paper describes the status of the real-time broadband RFI excision system in the wideband receiver chain of the upgraded GMRT. The methodology for carrying out various tests to demonstrate the performance of broadband RFI excision at the system level and on the radio astronomical imaging experiments is also described.