Types Of Radio Frequency Interference Research Articles

Research on RFI Mitigation Methods for Fast Radio Burst Observational Data

To quickly search for rare fast radio bursts (FRBs) from massive astronomical observational data, Radio Frequency interference (RFI) mitigation is one of the key and challenging task. RFI will cause search algorithm to output a large number of false positive candidates, and even submerges real astronomical events. Due to the complexity of the sources and types of RFI. there is currently no universal method to solve this problem. In order to reduce the impact of RFI on FRB search, the RFI in L band observational data of the Nanshan 26m radio telescope (NSRT-26m) was analyzed and studied. A three-layer RFI mitigation procedure was established for the main narrowband RFI and broadband RFI, which effectively alleviated the RFI pollution of observational data. Embedding this procedure into the FRB DDSS (Dispersion Dynamic Spectra Search) pipeline, experimental results show that the detection rate and accuracy of the search algorithm have been further improved. This method provides valuable reference for RFI mitigation of FRB observational data.

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.

A Deep Dive into De-Doppler Algorithms for SETI

Narrowband SETI algorithms have been in use for many decades. A key processing step in the usual SETI pipeline called “De-Doppler” involves non-coherent integration of linearly-drifting lines in a spectrogram. De- Doppler (DD) is needed because a transmitted constant-frequency tone will be received as a chirp, or a tone with a near-constant frequency rate, due to relative accelerations between the transmitter and receiver. The frequency drift may be an important discriminator against certain types of radio-frequency interference. This paper examines the standard Taylor DD algorithm and finds there are significant signal losses in the integration process. Part of the loss is caused by imperfect indexing in the algorithm. Additional loss is associated with the effect of an FFT filter bank on chirp signals; DD performance is intertwined with the filter bank. Filter bank improvements involving polyphase filter banks are examined. A new DD algorithm called “fastDD” is proposed, as well as extensions to the Taylor algorithm. They both offer significant performance gains and higher resolution in frequency and drift rate in the DD detection plane. The loss mechanisms are now understood. Computational cost trade-offs for various filter bank and DD parameters are presented based on C code functions. Several “best” options are given which offer significant SNR improvements ranging from 2 to 4 dB for modest levels of additional computation. These options have the potential to dramatically improve detection rates.

Radio Frequency Interference Mitigation via Nonnegative Matrix Factorization for GNSS

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

Robust RFI Excision for Pulsar Signals by a Novel Nonlinear M-type Estimator with an Application to Pulsar Timing

Radio frequency interference (RFI) mitigation for pulsar signals is a long perplexing issue in astrophysical measurements. Linear mitigation methods are often criticized for limited RFI excision range and weakness of RFI modeling. Meanwhile, thresholding methods (e.g., the SumThreshold) suffer from empirical factors. In our opinion, the main defect of the current status is the lack of a concise definition to distinguish signals from RFI with the aid of certain techniques, e.g., sparse representation. This point is the root cause of these problems and also forms our motivation. This paper aims to expand the excision range (e.g., the on-pulse and quasiperiodic RFI) and cut down some empirical factors. The main contribution is that we give a definition and derive a widely practicable nonlinear framework for RFI excision. This framework can overcome the susceptibility of the least-square criterion to RFI, and excise almost all types of RFI once and for all. A robust LnCosh criterion based nonlinear maximum likelihood-type (M-type) penalized smoothing estimator is introduced. The novelty is that this estimator is first embedded into the iterative shrinkage-thresholding algorithm (ISTA) and the fast ISTA. Nonlinearity highlights this method. Curvelet sparsity gives satisfying approximation for pulsar signals containing dispersion feature. Finally, useful signal details will be retrieved from the excision residual by a morphological component analysis. This method is applied to the time-frequency signals collected by the Nanshan 26 m Radio Telescope. The numerical experiments can persuasively prove that it has desired application prospects.

Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks

Passive remote sensing is a crucial technology for climate studies and Earth science. NASA's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400-1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio-frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.

Detecting pulsars with neural networks: a proof of concept

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

Radio frequency interference mitigation based on the asymmetrically reweighted penalized least squares and SumThreshold method

ABSTRACT As radio telescopes become more sensitive, radio frequency interference (RFI) is becoming more important for interesting signals of radio astronomy. There is a demand for developing an automatic, accurate and efficient RFI mitigation method. Therefore, we have investigated an RFI detection algorithm. First, we introduce an asymmetrically reweighted penalized least squares (ArPLS) method to estimate the baseline more accurately. After removing the estimated baseline, several novel strategies were proposed based on the SumThreshold algorithm for detecting different types of RFI. The threshold parameter in SumThreshold can be determined automatically and adaptively. The adaptiveness is essential for reducing human intervention and for the online RFI processing pipeline. Applications to data from the Five-hundred-meter Aperture Spherical Telescope (FAST) show that the proposed scheme based on ArPLS and SumThreshold is superior to some typically available methods for RFI detection with respect to efficiency and performance.

An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom

The Quality of Service (QoS) and security of Satellite Communication (Satcom) are crucial as Satcom plays a significant role in a wide range of applications, such as direct broadcast satellite, earth observation, navigation, and government/military systems. Therefore, it is necessary to ensure that transmissions are incorruptible, particularly in the presence of challenges such as Radio Frequency Interference (RFI), which is of primary concern for the efficiency of communications. The security of a wireless communication system can be improved using a robust RFI detection method, which could, in turn, lead to an effective mitigation process. This paper presents a new method to recognize received signal characteristics using a hierarchical classification in a multi-layer perceptron (MLP) neural network. The considered characteristics are signal modulation and the type of RFI. In the experiments, a real-time video stream transmitted in the direct broadcast satellite is utilized with four modulation types, namely, QPSK, 8APSK, 16APSK, and 32APSK. Moreover, it is assumed that the communication signal can be combined with one of the three significant types of interference, namely, Continuous Wave Interference (CWI), Multiple CWI (MCWI), and Chirp Interference (CI). In addition, two robust feature selection techniques have been developed to select more informative features, which leads to improving the classification precision. Furthermore, the robustness of the trained techniques is assessed to predict unknown signals at different Signal to Noise Ratios (SNRs).

Multiclass Radio Frequency Interference Detection and Suppression for SAR Based on the Single Shot MultiBox Detector.

Radio frequency interference (RFI) is known to jam synthetic aperture radar (SAR) measurements, severely degrading the SAR imaging quality. The suppression of RFI in SAR echo signals is usually an underdetermined blind source separation problem. In this paper, we propose a novel method for multiclass RFI detection and suppression based on the single shot multibox detector (SSD). First, an echo-interference dataset is established by randomly combining the target signal with various types of RFI in a simulation, and the time–frequency form of the dataset is obtained by utilizing the short-time Fourier transform (STFT). Next, the time–frequency dataset acts as input data to train the SSD and obtain a network that is capable of detecting, identifying and estimating the interference. Finally, all of the interference signals are exactly reconstructed based on the prediction results of the SSD and mitigated by an adaptive filter. The proposed method can effectively increase the signal-to-interference-noise ratio (SINR) of RFI-contaminated SAR echoes and improve the peak sidelobe ratio (PSLR) after pulse compression. The simulated experimental results validate the effectiveness of the proposed method.

A new implementation of narrowband interference detection, characterization, and mitigation technique for a software-defined multi-GNSS receiver

Due to the very low power of satellite signals when reaching the earth’s surface, global navigation satellite system receivers are vulnerable to various types of radio frequency interference, and, therefore, countermeasures are necessary. In the case of a narrowband interference (NBI), the adaptive notch filtering technique has been extensively investigated. However, the research on the topic has focused on the adaptation of the notch frequency, but not of the notch width. We present a fully adaptive solution to counter NBI. The technique is capable of detecting and characterizing any number of narrow interfered bands, and then optimizing the mitigation process based on such characterization, namely the estimates of both interference frequency and width. Its full adaptiveness makes it suitable to cope with the unpredictable and diverse nature of unintentional interfering events. In addition to a thorough performance evaluation of the proposed method, which shows its benefits in terms of signal quality improvement, an analysis of the impact of different NBI profiles on GPS L1 C/A and Galileo E1 is also conducted.

An Autocorrelation-Based Radio Frequency Interference Detection and Removal Method in Azimuth-Frequency Domain for SAR Image

In this paper, the authors propose a novel radio frequency interference (RFI) detection and removal method for a synthetic aperture radar (SAR). The proposed method is exclusively sensitive to a pulse-like signal that is temporally transmitted in wide bandwidth. The bandwidth of the RFI is supposed to be similar or wider than the bandwidth of the SAR, while traditional RFI detectors assume that it is narrower and thus it can be removed in the range-frequency and azimuth-time domain using a bandpass filter. On the other hand, the proposed method employs a local autocorrelation-based detection scheme in the range-time and azimuth-frequency domain. The proposed method assumes the existence of another type of RFI, namely, intermittently transmitted wideband (ITWB) RFI. Once ITWB RFI superimposes on the SAR image, its short-pulse duration/wide bandwidth hinders the detection in the range-frequency and azimuth-time domain. Conversely, in the range-time and azimuth-frequency domain, ITWB RFI can be detected easily because its Doppler frequency profile does not fit with backscattered signals from the ground. In this paper, a basic methodology and the experimental results are presented.

Comparison of Polarimetric Algorithm With Other Algorithms in Detecting Radio Frequency Interference

Anthropogenic radio frequency interference (RFI) is an increasing problem in microwave remote sensing radiometry. Therefore, there is growing interest in RFI counteraction methods, including detection and blanking. In this study, the performance of polarimetric algorithm has been studied and compared with several other detection algorithms with respect to their capability to detect different potential types of RFI, such as continuous wave (CW), pulse modulated, and quadrature phase shift keying (QPSK) modulated. The mission scenario and parameters applied correspond to a typical space-borne, polar-orbiting, conically scanning microwave radiometer. Therefore, the results obtained help to estimate the achievable RFI detection limits (in terms of the brightness temperature) for similar future satellite instruments if polarimetry or other algorithms analyzed here were applied. The results obtained suggest that the RFI detection limit in the 1-K range can be achieved in the scenario under study. Incorporating multiple and complementary RFI detection algorithms in the overall system, e.g., in a digital RFI processor, enables a wide range of different RFI types to be detected. The results suggest that polarimetry and spectral density estimation are sensitive in detecting constant envelope signals (such as CW or QPSK), while kurtosis and spectral kurtosis are effective in detecting pulsed RFI. A detection limit as low as 0.1 K can be achieved for pulsed RFI with low duty cycles. These findings confirm and extend earlier results. To the best knowledge of the authors, this is the first quantitative study on the performance of the RFI detection algorithms to include polarimetry.

Comparison of the Radio Frequency Interference (RFI) in the Region of Solar Burst Type III Data at Selected CALLISTO Network

Compact Astronomical Low-frequency, Low-cost Instrument for Spectroscopy in Transportable Observatories (CALLISTO) is a global network of spectrometer system with the purpose to observe the Sun’s activities. There are 25 stations that are used for this purpose. Radio Frequency Interference (RFI) is a major obstacle when performing observation with CALLISTO. We have confirmed at least 2 stations out of 10 stations with a complete overview spectral (OVS) made available to us showed clear detection of these consistent types of RFI for each specific region. In Malaysia, these RFI are also clearly detected. The major RFI affecting CALLISTO within radio astronomical windows below 1 GHz are local electronic system specifically radio navigation (at 73.1 MHz and 75.2 MHz), broadcasting (at (i) 151 MHz, (ii) 151.8 MHz and 152 MHz), aeronautical navigation (at (i) 245.5 MHz, (ii) 248.7 MHz and (iii) 249 MHz and fixed mobile at (i) 605 MHz, (ii) 608.3 MHz, (iii) 612.2 MHz, (iv) 613.3 MHz). It is obviously showed that all sites within this region are free from interference at 320-330 MHz and is the best specific region to be considered for solar burst monitoring. We also investigate the effect of RFI on detection of solar burst. We have considered type III solar bursts on 9th March 2012 in order to measure the percentage of RFI level during the solar burst. The RFI level is as low as 6.512% to 80.769% above solar burst detection.

Comparison of the Radio Frequency Interference (RFI) in the Region of Solar Burst Type III Data at Selected CALLISTO Network

Compact Astronomical Low-frequency, Low-cost Instrument for Spectroscopy in Transportable Observatories (CALLISTO) is a global network of spectrometer system with the purpose to observe the Sun’s activities. There are 25 stations that are used for this purpose. Radio Frequency Interference (RFI) is a major obstacle when performing observation with CALLISTO. We have confirmed at least 2 stations out of 10 stations with a complete overview spectral (OVS) made available to us showed clear detection of these consistent types of RFI for each specific region. In Malaysia, these RFI are also clearly detected. The major RFI affecting CALLISTO within radio astronomical windows below 1 GHz are local electronic system specifically radio navigation (at 73.1 MHz and 75.2 MHz), broadcasting (at (i) 151 MHz, (ii) 151.8 MHz and 152 MHz), aeronautical navigation (at (i) 245.5 MHz, (ii) 248.7 MHz and (iii) 249 MHz and fixed mobile at (i) 605 MHz, (ii) 608.3 MHz, (iii) 612.2 MHz, (iv) 613.3 MHz). It is obviously showed that all sites within this region are free from interference at 320-330 MHz and is the best specific region to be considered for solar burst monitoring. We also investigate the effect of RFI on detection of solar burst. We have considered type III solar bursts on 9th March 2012 in order to measure the percentage of RFI level during the solar burst. The RFI level is as low as 6.512% to 80.769% above solar burst detection.

Computationally Efficient RF Interference Suppression Method With Closed-Form Maximum Likelihood Estimator for HF Surface Wave Over-The-Horizon Radars

Among all types of unwanted signals in high-frequency (HF) surface wave (HFSW) over-the-horizon (OTH) radars, radio-frequency interference (RFI) is dominant since HF band is shared by many radio services. In observation data, there are two types of common RFI. The most common one is the conventional RFI which presents vertical stripe paralleling to range axis in range-Doppler spectrum (RDS) and has been exhaustively reported by previous papers. Meanwhile, a new type of RFI characterized by sloping stripes (called RFI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SS</sub> ) in RDS is also frequently observed. This work concentrates on the new RFI <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SS</sub> and establishes a unified model for the above two types of RFI. Based on this generalized model, a time-domain RFI suppression algorithm is proposed here. Benefiting from a closed-form approximate maximum likelihood estimator, the proposed algorithm exhibits excellent performance and is computationally efficient. Its operational performance is evaluated using the field data recorded by experimental HFSW OTH radar of Wuhan University.

Radio frequency interference suppression in high frequency surface wave radar based on range–domain correlation

High frequency surface wave radar (HFSWR) is often subject to the serious impact of external radio frequency interference (RFI), which must be suppressed efficiently in order to improve the viability of the radar. In this paper, we consider two different types of RFIs. One has a strong range–domain correlation at all range cells, while the other has a strong range–domain correlation only at the adjacent range cells. According to the difference on range–domain correlation between these two types of RFIs, we propose two different suppression methods based on the range–domain adaptive filtering. One is the far range cells method which uses the signals at the far range cells as the secondary data, and the other is the adjacent range cells method which uses the signals at the adjacent range cells as the secondary data. In the practical processing, we should select the far range cells method when the RFI has a strong range–domain correlation at all range cells. Otherwise, we should select the adjacent range cells method. Processing results of real data are presented to demonstrate the effectiveness of these two methods.

A Wideband Spectrometer with RFI Detection

ABSTRACTWe report on the design and construction of a wideband spectrometer of 500 MHz instantaneous bandwidth that includes automatic radio frequency interference (RFI) detection. The implementation is based on hardware developed at the Center for Astronomical Signal Processing and Electronics Research (CASPER). The unique aspect of the spectrometer is that it accumulates both power and power-squared, which are then used to develop a spectral kurtosis (SK) estimator. The SK estimator statistics are used for real-time detection and excision of certain types of RFI embedded in the received signal. We report on the use of this spectrometer in the Korean Solar Radio Burst Locator (KSRBL). This instrument utilizes four of these 500 MHz bandwidth SK spectrometers in parallel, to achieve a 2 GHz instantaneous bandwidth that is time multiplexed over the entire 0.24–18 GHz radio frequency range, to study solar bursts. The performance of the spectrometers for excising RFI over this range is presented. It is found that the algorithm is especially useful for excising highly intermittent RFI but is less successful for RFI due to digital signals. A method we call multiscale SK is presented that addresses the known blindness of Kurtosis-based estimators to 50% duty-cycle RFI. The SK algorithm can also be applied to spectral channels prior to correlation to remove unwanted RFI from interferometer data.