Radio Frequency Interference Suppression Research Articles

Synthetic Aperture Radar Radio Frequency Interference Suppression Method Based on Fusing Segmentation and Inpainting Networks

Synthetic Aperture Radar (SAR) is a high-resolution imaging sensor commonly mounted on platforms such as airplanes and satellites for widespread use. In complex electromagnetic environments, radio frequency interference (RFI) severely degrades the quality of SAR images due to its widely varying bandwidth and numerous unknown emission sources. Although traditional deep learning-based methods have achieved remarkable results by directly processing SAR images as visual ones, there is still considerable room for improvement in their performance due to the wide coverage and high intensity of RFI. To address these issues, this paper proposes the fusion of segmentation and inpainting networks (FuSINet) to suppress SAR RFI in the time-frequency domain. Firstly, to weaken the dominance of RFI in SAR images caused by high-intensity interference, a simple CCN-based network is employed to learn and segment the RFI. This results in the removal of most of the original interference, leaving blanks that allow the targets to regain dominance in the overall image. Secondly, considering the wide coverage characteristic of RFI, a U-former network with global information capture capabilities is utilized to learn the content covered by the interference and fill in the blanks created by the segmentation network. Compared to the traditional Transformer, this paper enhances its global information capture capabilities through shift-windows and down-sampling layers. Finally, the segmentation and inpainting networks are fused together through a weighted parameter for joint training. This not only accelerates the learning speed but also enables better coordination between the two networks, leading to improved RFI suppression performance. Extensive experimental results demonstrate the substantial performance enhancement of the proposed FuSINet. Compared to the PISNet+, the proposed attention mechanism achieves a 2.49 dB improvement in peak signal-to-noise ratio (PSNR). Furthermore, compared to Uformer, the FuSINet achieves an additional 4.16 dB improvement in PSNR.

Synthetic aperture radar radio frequency interference suppression technology

Radio frequency interference (RFI) can easily hinder the data collection, imaging and interpretation of the initial echo of synthetic aperture radar, which has always been a difficult problem affecting the accuracy of synthetic aperture radar. In this paper, the radio frequency interference suppression technology of synthetic aperture radar is analyzed and studied. It can be concluded that in todays complex and changeable electromagnetic environment, although many literature have proposed and established different objective functions and constraints for more specific application environments, there is still room for further improvement. Therefore, the future SAR system needs to select the most appropriate interference suppression method according to the dynamic changes of RFI environment. The development trend of interference suppression technology in the future is obtained from the comprehensive analysis of the RFI interference sources, RFI models and existing SAR interference suppression technologies, which provides a certain reference for future research.

A Comparative Study on Radio Frequency Interference Suppression and foF2 Scaling for Ionograms

In this paper, we compare the performances of radio frequency interference (RFI) suppression between radio frequency interference mitigation (RFIM) and frequency-domain adaptive clutter suppression (FACS) algorithms that apply to ionograms for foF2 scaling through true height analysis. We find that the use of RFIM and FACS can both effectively suppress RFIs in ionograms to improve the signal-to-clutter (SCR) of the original ionogram. Overall, the RFI clutter suppressed by FACS is about 5–7 dB better than that by RFIM. In addition, the SCR improvement made by FACS with neighboring range cells in a range of 10–40 is also better than that made by RFIM, which is about 5–10 dB for FACS and 4–8 dB for RFIM, respectively. However, the signal-to-noise ratios (SNRs) of the ionospheric echoes of the ionograms with FACS are about 3–5 dB (2–3 dB) smaller than those of RFIM during daytime (nighttime). Intercomparisons reveal that the correlations of the scaled foF2 values between the original and RFIM ionograms are slightly higher than those between the original and FACS ionograms, while the root-mean-square-error (RMSE) and mean bias of the former are slightly larger than the latter. These results indicate that the use of RFIM and FACS algorithms to suppress RFIs in ionograms can conditionally improve the quality of scaled foF2 using Ke et al.’s autoscaling algorithm. Statistical results reveal that the use of FACS algorithms can result in about a 5–12% improvement in uncertainty and a 9–80% improvement in accuracy, provided that 5–20 neighboring range cells are selected. As for the RFIM algorithm with a threshold of 2–2.5 times standard deviation, the improvements in uncertainty and accuracy are, respectively, about 0.9–10% and 11–90%.

Joint Radio Frequency Interference and Deceptive Jamming Suppression Method for Single-Channel SAR via Subpulse Coding

The radio frequency interference (RFI) and deceptive jamming (DJ), as two major external threats to synthetic aperture radar (SAR) systems, can greatly reduce the readability and veracity of the obtained SAR images. Current interference suppression methods have no capability to suppress both of them. In this article, a subpulse coding (SPC)-based joint RFI and DJ suppression method for single-channel SAR systems is proposed. By making full use of the elaborate coding scheme and subpulse transmitting mode, SPC can effectively suppress RFIs in the Doppler domain. On the other hand, after the decoding process, by utilizing the subpulse digital beamforming (DBF) technology with the well-designed DBF weight vectors, DJs can also be suppressed greatly. Numerical experiments verify the effectiveness of the proposed method.

MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform

Spaceborne synthetic aperture radar (SAR) is a promising remote sensing technique, as it can produce high-resolution imagery over a wide area of surveillance with all-weather and all-day capabilities. However, the spaceborne SAR sensor may suffer from severe radio frequency interference (RFI) from some similar frequency band signals, resulting in image quality degradation, blind spot, and target loss. To remove these RFI features presented on spaceborne SAR images, we propose a multi-dimensional calibration and suppression network (MCSNet) to exploit the features learning of spaceborne SAR images and RFI. In the scheme, a joint model consisting of the spaceborne SAR image and RFI is established based on the relationship between SAR echo and the scattering matrix. Then, to suppress the RFI presented in images, the main structure of MCSNet is constructed by a multi-dimensional and multi-channel strategy, wherein the feature calibration module (FCM) is designed for global depth feature extraction. In addition, MCSNet performs planned mapping on the feature maps repeatedly under the supervision of the SAR interference image, compensating for the discrepancies caused during the RFI suppression. Finally, a detailed restoration module based on the residual network is conceived to maintain the scattering characteristics of the underlying scene in interfered SAR images. The simulation data and Sentinel-1 data experiments, including different landscapes and different forms of RFI, validate the effectiveness of the proposed method. Both the results demonstrate that MCSNet outperforms the state-of-the-art methods and can greatly suppress the RFI in spaceborne SAR.

Dance Movement Interference Suppression Algorithm Based upon Contour Model and AdaBoost Algorithm

With the development of computer technology, digital image processing, as an emerging field, has become an indispensable tool in the information society. The action interference effect refers to the phenomenon that an individual reacts to a dangerous object longer than a safe object in the state of preparation for action. Currently, there is no effective anti-jamming technology to recognize dance movements. In order to solve these problems, this paper proposes a dance movement interference suppression algorithm based on contour model and AdaBoost algorithm, which aims to realize the application of gesture-based behavior recognition technology in the field of dance steps and complete the recognition of some dance steps. The method of this paper is to study the contour model based on the image and then propose to use the AdaBoost algorithm to detect body movements and finally use the radio frequency interference suppression algorithm to ensure the smooth operation of the technology. The function of the method is to propose a dance action recognition and anti-interference algorithm based on contour model and AdaBoost algorithm. This article describes the contour model and then uses the AdaBoost algorithm to recognize the action and adjust the action interference effect. The results show that the algorithm proposed in this paper can accurately identify dance movements, with an average recognition accuracy rate of 99.2%.

Ultrathin, Electrically Small Noise Suppression Sheet for Microwave Cavities of 3-D Integrated Circuits: Design Methodology and Realization

Miniaturized microwave cavities constructed by the parallel-plate waveguide (PPW) becomes one of the main propagation pathways of the radio-frequency interference (RFI) in 3-D integrated circuits. It is still challenging to suppress RFI in the practical cavities, where the space is extremely constrained. In this work, the working mechanism and design method of the noise suppression sheet (NSS) is quantitatively studied, serving as an efficient approach to suppress RFI within an electrically small size in these microwave cavities. The analysis is based on the dispersion relationship derivation of the NSS-loaded PPW structure. Both the derived formulas and the numerical study reveal that the imaginary part of the permeability, that is, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu _{i}$ </tex-math></inline-formula> , of the NSS plays a key role in increasing the reflection and attenuation loss, and thus obtaining a high RFI suppression level. Further study indicates even when the NSS’s profile is lower than 10% of the cavity’s height and the length is lower than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.1\lambda _{0}$ </tex-math></inline-formula> , its suppression level up to 10 dB can be well accomplished, which is deemed difficult to achieve by using conventional methods. Moreover, this method is, respectively, applied in three practical engineering scenarios for the RFI suppression, that is, heatsink package above a chip, power distributed network (PDN), and flexible circuit. Good agreement is observed between the measurement results and the simulated ones, which confirmed the efficacy of our quantitatively developed NSS. The proposed method is promising for broad industrial applications, such as tiny chip packages, multi-channel systems, and flexible electronics.

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.

Accurate SAR Image Recovery From RFI Contaminated Raw Data by Using Image Domain Mixed Regularizations

Radio frequency interference (RFI) suppression is a hot topic in synthetic aperture radar (SAR) imaging. Mathematically, the RFI suppression problem can be considered as an underdetermined signal separation problem to extract the signal of interest (SOI) from the RFI contaminated raw data. The regularization-based method can exploit both the prior knowledge of RFI and SOI and, therefore, has the advantage of solving the underdetermined problem and preserving the information of SOI. Current regularization methods make use of the RFI prior well by exploiting low-rank representation (LRR) or sparse representation (SR), but the prior knowledge of SOI has not been sufficiently studied and used. In some literature, the sparsity of the raw data or range profile was exploited to formulate the regularization term, which we found to be inadequate in describing the SOI property. In this article, we explore the features of SAR images and propose an RFI suppression model with a combination of multiple image domain regularizations to preserve different types of targets. An efficient solution to the optimization problem is proposed based on the alternating direction multiplier method (ADMM). The proposed method can accurately recover both the sparse strong targets, and the nonsparse regions in the illuminated area and its performance is validated by measured data.

Learning Time–Frequency Information With Prior for SAR Radio Frequency Interference Suppression

In the complex electromagnetic environment, radio frequency interference (RFI) from other radiation sources often conflicts with synthetic aperture radar (SAR) systems, which overlaps and destroys the useful data in the same frequency band, causing adverse impact to the quality of SAR imaging. When faced with wideband or mixed complicated RFI, traditional methods inevitablely damage the original signal, and cannot effectively protect and reconstruct the useful information. Besides, the current semi-parametric algorithms have large computations and limited generalization ability. To address these issues, this paper proposes a prior-induced-learning framework (PISNet) to achieve RFI suppression and useful signal recovery in time-frequency domain. Both narrowband and wideband interference are uniformly modeled as a sparse distribution in time-frequency domain, and the stationarity of SAR echoes determines its low-rank characteristic. These properties of RFI and SAR data are treated as prior knowledge to inject into our PISNet. An iterative reconstruction module is raised to achieve low-rank reorganization of the fused residual features. Meanwhile, a novel loss function is put forward to induce the network training to ensure that each component conform to the prior. The proposed approach innovatively integrates deep learning with semi-parametric methods for RFI suppression, which achieves superior performance on simulated and real data. Compared to existing learning-based methods, the image quality of the restored Sentinel-1 data is improved by 9.37% AG. The code and dataset will be available online (https://github.com/JyuanShen/PISNet).

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.

Design and development of radio frequency system for pulse positron low energy beam and its electron beam trials

Details of the design and development of radio frequency (RF) system at 37.5 MHz and 150 MHz and initial test results of electron beam trials of a pulsed positron beam line being developed at BARC, Mumbai are presented in this paper. The RF part of the beam line is based on the conventional positron pulsing technique of chopper-pre buncher-buncher arrangement. Picosecond (ps) pulsing is achieved by a 150 MHz quarter wave resonator, which times focuses the chopped positron beam onto the sample. The chopper and pre buncher, preceding the buncher, are designed to operate at one-fourth of the frequency of the buncher. The reflection type grid chopper uses sinusoidal waves riding on a DC offset to chop the incoming beam into less than 2 ns pulses. These various structures were excited by the RF power coupled from an overall two frequency RF system. This RF system comprises of amplifiers at 37.5 MHz and 150 MHz that derive a single common input from an arbitrary frequency generator (AFG) to reduce the timing distortion, their respective impedance matching units and RF coupling lines made of RF components used for amplitude and phase adjustments and then, feeds the RF power to these structures. The RF system was initially tested on a prototype setup before incorporating in the main pulsed positron beam line. RF interference suppression techniques like filtering and shielding were used to ensure minimum interference between RF signals and the various detector electronics. Before positron beam trials, electron beam trials were performed in the positron beam line. A final pulse width 280 ps was measured with an electron beam.

Feasible Evaluation of Shunt Active Filter for Harmonics Mitigation in Induction Heating System

This paper propounds the incorporation of a three-level inverter based Shunt Active Filter (SAF) in the Induction Heating (IH) system to eradicate the problems due to Electromagnetic Interference (EMI) &amp; Radio Frequency Interference (RFI). The IH system generates a considerable amount of high-frequency harmonics because of myriad causes, the predominant one being the high-frequency switching in the resonant inverter. The former has an immanent propensity to flow towards the supply side and results in the enfeeblement of power quality. Moreover, in the present work, attention has been paid off to develop a proper control strategy of a three level inverter based SAF for EMI and RFI suppression. A new modeling approach of three-level inverter based SAF is proposed and the efficacy and viability of the proposed controllers for SAF in the IH system are validated via simulations in PSIM. A comparative analysis of THD in the input current waveform has been done to advocate the desideratum of SAF as an imperative part of the IH system. Results obtained by simulations show that the proposed approach is more effective than the reviewed approaches on compensating the harmonic currents and thus, the filtering action of SAF is able to achieve the THD of input current within the limit specified by the IEEE-519 standard. Doi: 10.28991/HIJ-2021-02-03-08 Full Text: PDF

The research on notch filtering and spectral filling for RFI suppression

Radio frequency interference (RFI) suppression has become an essential step in synthetic aperture radar (SAR) imaging processing. In this paper, a spectral filling filtering method for RFI suppression referred to notch filter is derived and analysed. The derivation results show the reason for the “residual terms” after suppression processing in frequency domain and the influence of the “residual terms”. And a conclusion is drawn: the essence of interference suppression based on spectral filling filter is the dynamic change process of “residual terms”. Since they cannot be completely eliminated in theory, they will affect the image quality after interference suppression, but the effect of spectral filling method is better than that of the notch filter method. The conclusion is verified by the simulation experiment.

Multiple RFI Sources Location Method Combining Two-Dimensional ESPRIT DOA Estimation and Particle Swarm Optimization for Spaceborne SAR

Synthetic aperture radar (SAR) systems are susceptible to radio frequency interference (RFI). The existence of RFI will cause serious degradation of SAR image quality and a huge risk of target misjudgment, which makes the research on RFI suppression methods receive widespread attention. Since the location of the RFI source is one of the most vital information for achieving RFI spatial filtering, this paper presents a novel location method of multiple independent RFI sources based on direction-of-arrival (DOA) estimation and the non-convex optimization algorithm. It deploys an L-shaped multi-channel array on the SAR system to receive echo signals, and utilizes the two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) algorithm to estimate the positional relationship between the RFI source and the SAR system, ultimately combines the DOA estimation results of multiple azimuth time to calculate the geographic location of RFI sources through the particle swarm optimization (PSO) algorithm. Results on simulation experiments prove the effectiveness of the proposed method.

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.

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.

SAR RFI Suppression for Extended Scene Using Interferometric Data via Joint Low-Rank and Sparse Optimization

Radio frequency interference (RFI) can significantly pollute synthetic aperture radar (SAR) data and images, which is also harmful to SAR interferometry (InSAR) for retrieving elevational information. To address this issue, in recent years, a class of advanced RFI suppression methods has been proposed based on narrowband properties of RFI and sparsity assumptions of radar echoes or target reflectivity. However, for SAR echoes and the associated scene reflectivity, these assumptions are usually not feasible when the imaged scene is spatially extended. In view of these problems, this study proposes an InSAR-based RFI suppression method for the case of extended scenes. For this task, we combine the RFI-polluted SAR data with RFI-free interferometric data to form an interferometric SAR data pair. We show that such an InSAR data pair embeds an interferogram having the image amplitude multiplying by a complex exponential interferometric phase. We treat the interferogram as a kind of natural image and use discrete Fourier cosine transform (DCT) for its sparse representation. Then combining the DCT-domain sparsity with low-rank modeling of RFI, we retrieve the interferogram and reconstruct the SAR image via joint low-rank and sparse optimization. Numerical simulations show that the proposed method can effectively recover SAR images and interferometric phases from RFI-polluted SAR data.

Enhanced LRR-Based RFI Suppression for SAR Imaging Using the Common Sparsity of Range Profiles for Accurate Signal Recovery

The performance of synthetic aperture radar is vulnerable to radio frequency interference (RFI). In many situations, the RFI has a low-rank property, since the frequency bands occupied by RFI usually remain stable during a short slow time period. Therefore, low-rank representation (LRR)-based methods can be applied to separate RFI and signal of interest (SOI), by minimizing the rank of RFI components with a regularization constraint to protect SOI. However, traditional methods use the sparsity of the raw data or range profile to formulate the regularization term, which fails to describe the properties of SOI accurately. In addition to the sparse property of range profiles, this article explores the common patterns hidden in the range profiles and proposes two new LRR-based RFI suppression optimization models with a well-designed regularization term to describe such common sparsity to protect the SOI. Four methods are proposed to solve the optimization problems based on the alternating direction multiplier (ADM) method, which provides tradeoff between efficiency and accuracy. Compared with traditional LRR-based RFI suppression methods, the proposed methods make a more precise description of the features of SOI, therefore can better protect the information of SOI during the RFI suppression process and improves the imaging quality. The superior performance of the proposed method is validated by measured data in both sparse and nonsparse scenes.

A Dictionary-Based SAR RFI Suppression Method via Robust PCA and Chirp Scaling Algorithm

Synthetic aperture radar (SAR) is an important imaging tool in many applications. Its imaging quality can be easily degraded by radio-frequency interferences (RFIs), among which the narrowband ones are typical. In recent years, it is shown that the narrowband RFI has a low-rank property and this property can be combined with the sparsity of radar echoes' to develop efficient RFI-suppression algorithms. However, these works usually consider the case of sparse echoes in the impulse-based radar, which is not suitable for typical SAR systems that use a chirp signal with a relatively long pulse duration. Some works adopt a large 2-D dictionary to introduce sparse representation for the echoes, which nevertheless lack efficient numerical algorithms, because the large dictionary brings high storage cost and computational burden. Motivated by these problems, this letter introduces an operator modeling approach for the echo dictionary and proposes a dictionary-based SAR RFI-suppression method under the framework of robust principle component analysis (RPCA). In the proposed method, the useful echo is sparsely represented by a dictionary, and the dictionary's analysis and synthesis operators are modeled as two sequences of low-cost operations by exploiting the chirp scaling algorithm. Then, an efficient algorithm is derived for solving the dictionary-based RPCA problem. Numerical simulations show that the proposed method is robust and efficient for SAR narrowband RFI suppression.