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Journal articles on the topic 'Radio-frequency interference detection'

Author: Grafiati

Published: 7 September 2021

Last updated: 9 February 2022

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1

Howard, William E. "Radio Frequency Interference Detection from Space." International Astronomical Union Colloquium 112 (1991): 198–200. http://dx.doi.org/10.1017/s0252921100004000.

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Occasional interference experienced in the channels of communications satellites has prompted an analysis to see how radio frequency interference (RFI) might be detected from space. RFI may be experienced on any type of satellite, including commercial and scientific satellites. For a satellite in geostationary orbit that interference may come from anywhere in the hemisphere under the satellite. Because the location of an interfering transmitter is so uncertain, traditional means for geolocating it is not effective. “Down-looking” detectors are needed to detect the “up-looking” interference. Moreover, a low cost, simple solution to the problem – one in which the cost to geolocate is small relative to the cost of the downtime in the channel – is required in order to make the solution tractable.

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Mosiane, Olorato, Nadeem Oozeer, Arun Aniyan, and Bruce A. Bassett. "Radio Frequency Interference Detection using Machine Learning." IOP Conference Series: Materials Science and Engineering 198 (May 2017): 012012. http://dx.doi.org/10.1088/1757-899x/198/1/012012.

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3

Yang, Zhicheng, Ce Yu, Jian Xiao, and Bo Zhang. "Deep residual detection of radio frequency interference for FAST." Monthly Notices of the Royal Astronomical Society 492, no. 1 (2020): 1421–31. http://dx.doi.org/10.1093/mnras/stz3521.

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ABSTRACT Radio frequency interference (RFI) detection and excision are key steps in the data-processing pipeline of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Because of its high sensitivity and large data rate, FAST requires more accurate and efficient RFI flagging methods than its counterparts. In the last decades, approaches based upon artificial intelligence (AI), such as codes using convolutional neural networks (CNNs), have been proposed to identify RFI more reliably and efficiently. However, RFI flagging of FAST data with such methods has often proved to be erroneous, with further manual inspections required. In addition, network construction as well as preparation of training data sets for effective RFI flagging has imposed significant additional workloads. Therefore, rapid deployment and adjustment of AI approaches for different observations is impractical to implement with existing algorithms. To overcome such problems, we propose a model called RFI-Net. With the input of raw data without any processing, RFI-Net can detect RFI automatically, producing corresponding masks without any alteration of the original data. Experiments with RFI-Net using simulated astronomical data show that our model has outperformed existing methods in terms of both precision and recall. Besides, compared with other models, our method can obtain the same relative accuracy with fewer training data, thus reducing the effort and time required to prepare the training data set. Further, the training process of RFI-Net can be accelerated, with overfittings being minimized, compared with other CNN codes. The performance of RFI-Net has also been evaluated with observing data obtained by FAST and the Bleien Observatory. Our results demonstrate the ability of RFI-Net to accurately identify RFI with fine-grained, high-precision masks that required no further modification.

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4

Misra, Sidharth, and Christopher S. Ruf. "Detection of Radio-Frequency Interference for the Aquarius Radiometer." IEEE Transactions on Geoscience and Remote Sensing 46, no. 10 (2008): 3123–28. http://dx.doi.org/10.1109/tgrs.2008.920371.

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Offringa, A. R., J. J. van de Gronde, and J. B. T. M. Roerdink. "A morphological algorithm for improving radio-frequency interference detection." Astronomy & Astrophysics 539 (February 29, 2012): A95. http://dx.doi.org/10.1051/0004-6361/201118497.

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Wu, Ying, and Fuzhong Weng. "Detection and correction of AMSR-E radio-frequency interference." Acta Meteorologica Sinica 25, no. 5 (2011): 669–81. http://dx.doi.org/10.1007/s13351-011-0510-0.

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7

Qu, Hong Quan, Jie Yang, and Hong Bin Qu. "Adaptive Radio Frequency (RF) Identification Based on Frequency Domain Analysis for Open Non-Gaussian Background Environment." Applied Mechanics and Materials 411-414 (September 2013): 1143–47. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1143.

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With the development of electronic technology, electronic equipments are suffering more electromagnetic interferences, so it is necessary to detect and evaluate their electromagnetic interference degree. The most commonly used method is the anechoic chamber test method that conducted in closed electromagnetic wave environment. However, there are some disadvantages, such as low detection efficiency and high testing cost. In order to avoid these advantages, a new method based on frequency domain analysis is presented to adaptively identify RF band of a detected device working in a non-Gaussian background environment. In this method, a non-parametric hypothesis testing is used to solve the interference of background noise on the detection result in an open environment. Further, an adaptive threshold hypothesis test method is also used to obtain the radio frequency spectrum of the detected signals for low SNR in order to overcome the frequency domain time-varying characteristics. The simulations show that the adaptive RF identification based on frequency domain analysis can improve the detection accuracy in the non-Gaussian background environment.

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Cucho‐Padin, G., Y. Wang, E. Li, et al. "Radio Frequency Interference Detection and Mitigation Using Compressive Statistical Sensing." Radio Science 54, no. 11 (2019): 986–1001. http://dx.doi.org/10.1029/2019rs006902.

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9

Misra, Sidharth, Priscilla N. Mohammed, Baris Guner, Christopher S. Ruf, Jeffrey R. Piepmeier, and Joel T. Johnson. "Microwave Radiometer Radio-Frequency Interference Detection Algorithms: A Comparative Study." IEEE Transactions on Geoscience and Remote Sensing 47, no. 11 (2009): 3742–54. http://dx.doi.org/10.1109/tgrs.2009.2031104.

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10

Yan, Rui-Qing, Cong Dai, Wei Liu, et al. "Radio frequency interference detection based on the AC-UNet model." Research in Astronomy and Astrophysics 21, no. 5 (2021): 119. http://dx.doi.org/10.1088/1674-4527/21/5/119.

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11

Cho, John Y. N. "A New Radio Frequency Interference Filter for Weather Radars." Journal of Atmospheric and Oceanic Technology 34, no. 7 (2017): 1393–406. http://dx.doi.org/10.1175/jtech-d-17-0028.1.

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AbstractA new radio frequency interference (RFI) filter algorithm for weather radars is proposed in the two-dimensional (2D) range-time/sample-time domain. Its operation in 2D space allows RFI detection at lower interference-to-noise or interference-to-signal ratios compared to filters working only in the sample-time domain while maintaining very low false alarm rates. Simulations and real weather radar data with RFI are used to perform algorithm comparisons. Results are consistent with theoretical considerations and show the 2D RFI filter to be a promising addition to the signal processing arsenal against interference with weather radars. Increased computational burden is the only drawback relative to filters currently used by operational systems.

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12

Brodrick, David, Douglas Taylor, and Joachim Diederich. "Recurrent Neural Networks for Narrowband Signal Detection in the Time-Frequency Domain." Symposium - International Astronomical Union 213 (2004): 483–86. http://dx.doi.org/10.1017/s0074180900193751.

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A recurrent neural network was trained to detect the time-frequency domain signature of narrowband radio signals against a background of astronomical noise. The objective was to investigate the use of recurrent networks for signal detection in the Search for Extra-Terrestrial Intelligence, though the problem is closely analogous to the detection of some classes of Radio Frequency Interference in radio astronomy.

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13

Same, Mohammad Hossein, Gabriel Gleeton, Gabriel Gandubert, Preslav Ivanov, and Rene Jr Landry. "Multiple Narrowband Interferences Characterization, Detection and Mitigation Using Simplified Welch Algorithm and Notch Filtering." Applied Sciences 11, no. 3 (2021): 1331. http://dx.doi.org/10.3390/app11031331.

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By increasing the demand for radio frequency (RF) and access of hackers and spoofers to low price hardware and software defined radios (SDR), radio frequency interference (RFI) became a more frequent and serious problem. In order to increase the security of satellite communication (Satcom) and guarantee the quality of service (QoS) of end users, it is crucial to detect the RFI in the desired bandwidth and protect the receiver with a proper mitigation mechanism. Digital narrowband signals are so sensitive into the interference and because of their special power spectrum shape, it is hard to detect and eliminate the RFI from their bandwidth. Thus, a proper detector requires a high precision and smooth estimation of input signal power spectral density (PSD). By utilizing the presented power spectrum by the simplified Welch method, this article proposes a solid and effective algorithm that can find all necessary interference parameters in the frequency domain while targeting practical implantation for the embedded system with minimum complexity. The proposed detector can detect several multi narrowband interferences and estimate their center frequency, bandwidth, power, start, and end of each interference individually. To remove multiple interferences, a chain of several infinite impulse response (IIR) notch filters with multiplexers is proposed. To minimize damage to the original signal, the bandwidth of each notch is adjusted in a way that maximizes the received signal to noise ratio (SNR) by the receiver. Multiple carrier wave interferences (MCWI) is utilized as a jamming attack to the Digital Video Broadcasting-Satellite-Second Generation (DVB-S2) receiver and performance of a new detector and mitigation system is investigated and validated in both simulation and practical tests. Based on the obtained results, the proposed detector can detect a weak power interference down to −25 dB and track a hopping frequency interference with center frequency variation speed up to 3 kHz. Bit error ratio (BER) performance shows 3 dB improvement by utilizing new adaptive mitigation scenario compared to non-adaptive one. Finally, the protected DVB-S2 can receive the data with SNR close to the normal situation while it is under the attack of the MCWI jammer.

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14

Camps, Adriano, Jerome Gourrion, Jose Miguel Tarongi, et al. "Radio-Frequency Interference Detection and Mitigation Algorithms for Synthetic Aperture Radiometers." Algorithms 4, no. 3 (2011): 155–82. http://dx.doi.org/10.3390/a4030155.

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15

Tarongi, Jose Miguel, and Adriano Camps. "Radio Frequency Interference Detection and Mitigation Algorithms Based on Spectrogram Analysis." Algorithms 4, no. 4 (2011): 239–61. http://dx.doi.org/10.3390/a4040239.

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16

Hogden, John, Scott Vander Wiel, Geoffrey C. Bower, Sarah Michalak, Andrew Siemion, and Daniel Werthimer. "COMPARISON OF RADIO-FREQUENCY INTERFERENCE MITIGATION STRATEGIES FOR DISPERSED PULSE DETECTION." Astrophysical Journal 747, no. 2 (2012): 141. http://dx.doi.org/10.1088/0004-637x/747/2/141.

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17

Bollian, Tobias, Batuhan Osmanoglu, Rafael F. Rincon, Seung-Kuk Lee, and Temilola E. Fatoyinbo. "Detection and Geolocation of P-Band Radio Frequency Interference Using EcoSAR." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 10 (2018): 3608–16. http://dx.doi.org/10.1109/jstars.2018.2830745.

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18

Li, Z., C. Yu, J. Xiao, M. Long, and C. Cui. "Detection of radio frequency interference using an improved generative adversarial network." Astronomy and Computing 36 (July 2021): 100482. http://dx.doi.org/10.1016/j.ascom.2021.100482.

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19

Sitompul, Peberlin Parulian, Timbul Manik, Mario Batubara, and Bambang Suhandi. "Radio Frequency Interference Measurements for a Radio Astronomy Observatory Site in Indonesia." Aerospace 8, no. 2 (2021): 51. http://dx.doi.org/10.3390/aerospace8020051.

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We report on the measurements of radio frequency interference (RFI) at Mount Timau, Kupang, Indonesia, which is intended to host a future radio astronomy observatory. These measurements were taken twice in October 2020 and December 2020 to obtain the RFI environment, at frequencies between 70 and 7000 MHz. Due to the limitations of the measurement data, the results presented in this paper are based on peak detection rather than statistical analysis. Based on the measurement results, the frequency interval between 70–88 MHz and 120–150 MHz is relatively quiet, and the frequency range of 150–300 MHz is relatively clear. The frequency interval of 300 to 800 MHz is relatively quiet, except at the frequency of 600 MHz. The frequency range of 800–1400 MHz is also relatively quiet. The predominant terrestrial services in this band are at 840 MHz, with an amplitude around 32 dB, and 916 MHz, with an amplitude around 12 dB, and the global system for mobile (GSM) signals around 954 MHz have an amplitude around 20 dB above the noise floor. The frequency range of 1400–7000 MHz is also relatively quiet. In this band frequency, we can see RFI at 2145 and 2407 MHz, emitted by local Wi-Fi, and at 2683 MHz, with amplitudes of 18, 40 and 15 dB, respectively, from the noise level. We conclude that, for this period, the frequency band allocated for astronomy can possibly be used for radio telescope development.

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20

Szadkowski, Zbigniew. "Least Mean Squares Filters Suppressing the Radio-Frequency Interference in AERA Cosmic Ray Radio Detection." IEEE Transactions on Nuclear Science 67, no. 1 (2020): 405–13. http://dx.doi.org/10.1109/tns.2019.2948319.

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21

Sun, Kewen, and Tengteng Zhang. "A New GNSS Interference Detection Method Based on Rearranged Wavelet–Hough Transform." Sensors 21, no. 5 (2021): 1714. http://dx.doi.org/10.3390/s21051714.

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Since radio frequency interference (RFI) seriously degrades the performance of a global navigation satellite system (GNSS) receiver, interference detection becomes very important for GNSS receivers. In this paper, a novel rearranged wavelet–Hough transform (RWHT) method is proposed in GNSS interference detection, which is obtained by the combination of rearranged wavelet transform and Hough transform (HT). The proposed RWHT method is tested for detecting sweep interference and continuous wave (CW) interference, the major types of GNSS interfering signals generated by a GNSS jammer in a controlled test bench experiment. The performance of the proposed RWHT method is compared with the conventional techniques such as Wigner–Ville distribution (WVD) and Wigner–Hough transform (WHT). The analysis results show that the proposed RWHT method reduces the influence of cross-item problem and improves the energy aggregation property in GNSS interference detection. When compared with the WHT approach, this proposed RWHT method presents about 90.3% and 30.8% performance improvement in the initial frequency and chirp rate estimation of the GNSS sweep interfering signal, respectively. These results can be further considered to be the proof of the validity and effectiveness of the developed GNSS interference detection method using RWHT.

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22

Ibrahim, Parrish, Brown, and McDonald. "Decision Tree Pattern Recognition Model for Radio Frequency Interference Suppression in NQR Experiments." Sensors 19, no. 14 (2019): 3153. http://dx.doi.org/10.3390/s19143153.

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Radio frequency interference places a major limitation on the in-situ use of unshielded nuclear quadrupole or nuclear magnetic resonance methods in industrial environments for quality control and assurance applications. In this work, we take the detection of contraband in an airport security-type application that is subject to burst mode radio frequency interference as a test case. We show that a machine learning decision tree model is ideally suited to the automated identification of interference bursts, and can be used in support of automated interference suppression algorithms. The usefulness of the data processed additionally by the new algorithm compared to traditional processing is shown in a receiver operating characteristic (ROC) analysis of a validation trial designed to mimic a security contraband detection application. The results show a highly significant increase in the area under the ROC curve from 0.580 to 0.906 for the proper identification of recovered data distorted by interfering bursts.

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23

Adami, Kristian Zarb, and I. O. Farhat. "Low-frequency technology for a lunar interferometer." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2188 (2020): 20190575. http://dx.doi.org/10.1098/rsta.2019.0575.

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This work sketches a possible design architecture of a low-frequency radio interferometer located on the lunar surface. The design has evolved from single antenna experiments aimed at the global signal detection of the epoch of reionization (EoR) to the square kilometre array (SKA) which, when complete, will be capable of imaging the highly red-shifted H 1 -signal from the cosmic dawn through to the EoR. However, due to the opacity of the ionosphere below 10 MHz and the anthropogenic radio-frequency interference, these terrestrial facilities are incapable of detecting pre-ionization signals and the moon becomes an attractive location to build a low-frequency radio interferometer capable of detecting such cosmological signals. Even though there are enormous engineering challenges to overcome, having this scientific facility on the lunar surface also opens up several new exciting possibilities for low-frequency radio astronomy. This article is part of a discussion meeting issue ‘Astronomy from the Moon: the next decades’.

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24

Yu, Junfei, Jingwen Li, Bing Sun, Jie Chen, and Chunsheng Li. "Multiclass Radio Frequency Interference Detection and Suppression for SAR Based on the Single Shot MultiBox Detector." Sensors 18, no. 11 (2018): 4034. http://dx.doi.org/10.3390/s18114034.

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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.

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25

Bower, Geoffrey C. "Radio frequency interference mitigation for detection of extended sources with an interferometer." Radio Science 40, no. 5 (2005): n/a. http://dx.doi.org/10.1029/2004rs003141.

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26

Mohammed, Priscilla N., Adam J. Schoenwald, Randeep Pannu, et al. "Detection of Radio Frequency Interference in Microwave Radiometers Operating in Shared Spectrum." IEEE Transactions on Geoscience and Remote Sensing 57, no. 9 (2019): 7067–74. http://dx.doi.org/10.1109/tgrs.2019.2911290.

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27

Zeng, Qingguo, Xue Chen, Xiangru Li, et al. "Radio frequency interference mitigation based on the asymmetrically reweighted penalized least squares and SumThreshold method." Monthly Notices of the Royal Astronomical Society 500, no. 3 (2020): 2969–78. http://dx.doi.org/10.1093/mnras/staa2551.

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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.

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28

Kocz, J., M. Bailes, D. Barnes, S. Burke-Spolaor, and L. Levin. "Enhanced pulsar and single pulse detection via automated radio frequency interference detection in multipixel feeds." Monthly Notices of the Royal Astronomical Society 420, no. 1 (2011): 271–78. http://dx.doi.org/10.1111/j.1365-2966.2011.20029.x.

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29

Islam, Tanvir, Prashant K. Srivastava, Qiang Dai, Manika Gupta, and Lu Zhuo. "An introduction to factor analysis for radio frequency interference detection on satellite observations." Meteorological Applications 22, no. 3 (2014): 436–43. http://dx.doi.org/10.1002/met.1473.

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30

Misra, Sidharth, and Christopher S. Ruf. "Analysis of Radio Frequency Interference Detection Algorithms in the Angular Domain for SMOS." IEEE Transactions on Geoscience and Remote Sensing 50, no. 5 (2012): 1448–57. http://dx.doi.org/10.1109/tgrs.2011.2176949.

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31

Zakharenko, V. V., V. B. Ryabov, I. P. Kravtsov, et al. "SPORADIC RADIO EMISSION OF SPACE OBJECTS AT LOW-FREQUENCIES." Radio physics and radio astronomy 26, no. 2 (2021): 99–129. http://dx.doi.org/10.15407/rpra26.02.099.

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Purpose: The results of studies of sporadic radio emission of several types of space radio sources, including neutron stars and planets of the Solar System, are presented. The aim of this work is to review the latest achievements in the study of low-frequency radio emission of the Solar System planets and transient signals similar to pulsar pulses using the UTR-2 radio telescope. The importance of the development of the verification methods of the spaceborne radio emission in the study of sporadic signals from various sources is shown. Design/methodology/approach: The studies of sporadic signals of different nature are based on the common set of procedures for cleaning records from the terrestrial radio frequency interference (RFI) in the frequency-time pattern using the information on the nature of the particular type of sporadic radio emission, possible types of interference and signal distortion. Characteristic features of sporadic radio emission of different sources are given, and for each of them the optimal method of signal extraction is developed. The efficiency of the developed procedures for cleaning from noise using adjustable parameters is shown. This is done on the basis of observations of diverse types of space radio sources, such as lightnings in the atmospheres of planets, single pulses of neutron stars and sporadic radio emission of Jupiter. Findings: The developed methods of signal extraction detection in the presence of radio-interference have allowed obtaining the unique results such as: the distribution of the total intensity of single pulses of neutron stars depending on the galactic latitude; the automatic search of lightnings in the Saturn’s atmosphere in data due to the carefully elaborated RFI mitigation procedures, which made it possible to obtain the Saturn electrostatic discharge (SED) emission parameters based on the most complete set of events. Increased efficiency of the selection of Jupiter’s S-radiation signals despite the data corruption by the presence of radio-interference, which was reached due to the carefully chosen parameters of data cleaning procedures, have allowed us to detect short and intensive bursts, being the most informative for determining the physical parameters of radio emission in the area of their generation. Conclusions: The large effective area and high sensitivity of the UTR-2 radio telescope allow making the sporadic radio emission study with high temporal and frequency resolutions. Due to these factors we can apply a wide range of methods of space signals’ detection in the presence of terrestrial radio-frequency interference of natural and artificial origin. The data cleaning parameters allow accounting for the characteristic features of space signals and to obtain important and even unique scientific results. Key words: decameter wavelength range; UTR-2; sporadic radio emission; transients; lightning in the atmospheres of planets; Jupiter S-bursts

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Natsuaki, Ryo, Takeshi Motohka, Manabu Watanabe, Masanobu Shimada, and Shinichi Suzuki. "An Autocorrelation-Based Radio Frequency Interference Detection and Removal Method in Azimuth-Frequency Domain for SAR Image." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 12 (2017): 5736–51. http://dx.doi.org/10.1109/jstars.2017.2775205.

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33

Ujan, Sahar, Neda Navidi, and Rene Jr Landry. "Hierarchical Classification Method for Radio Frequency Interference Recognition and Characterization in Satcom." Applied Sciences 10, no. 13 (2020): 4608. http://dx.doi.org/10.3390/app10134608.

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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).

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Guan Li, 官莉, and 黄安晶 Huang Anjing. "Detection of Radio-Frequency Interference over Land from AMSR-2 Observations at 7.3 GHz." Laser & Optoelectronics Progress 53, no. 2 (2016): 022802. http://dx.doi.org/10.3788/lop53.022802.

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35

de Andrade, Marcio C., Anna Leese de Escobar, Benjamin J. Taylor, et al. "Detection of Far-Field Radio-Frequency Signals by Niobium Superconducting Quantum Interference Device Arrays." IEEE Transactions on Applied Superconductivity 25, no. 5 (2015): 1–5. http://dx.doi.org/10.1109/tasc.2015.2470677.

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36

Wu, Ying, Bo Qian, Yansong Bao, et al. "Detection and Analysis of C-Band Radio Frequency Interference in AMSR2 Data over Land." Remote Sensing 11, no. 10 (2019): 1228. http://dx.doi.org/10.3390/rs11101228.

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

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Zhang, Zhongjun, Huimin Lan, and Tianjie Zhao. "Detection and mitigation of radiometers radio-frequency interference by using the local outlier factor." Remote Sensing Letters 8, no. 4 (2016): 311–19. http://dx.doi.org/10.1080/2150704x.2016.1266408.

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38

Baan, Willem A. "Implementing RFI Mitigation in Radio Science." Journal of Astronomical Instrumentation 08, no. 01 (2019): 1940010. http://dx.doi.org/10.1142/s2251171719400105.

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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.

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Misra, Sidharth, Jonathon Kocz, Robert Jarnot, et al. "Development of an On-Board Wide-Band Processor for Radio Frequency Interference Detection and Filtering." IEEE Transactions on Geoscience and Remote Sensing 57, no. 6 (2019): 3191–203. http://dx.doi.org/10.1109/tgrs.2018.2882306.

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Zou, Xiaolei, Juan Zhao, Fuzhong Weng, and Zhengkun Qin. "Detection of Radio-Frequency Interference Signal Over Land From FY-3B Microwave Radiation Imager (MWRI)." IEEE Transactions on Geoscience and Remote Sensing 50, no. 12 (2012): 4994–5003. http://dx.doi.org/10.1109/tgrs.2012.2191792.

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ZHAO, Juan, and Xiao-Ding YU. "Cross validations of radio-frequency interference signature in AMSR-E data using two detection methods." Atmospheric and Oceanic Science Letters 11, no. 3 (2018): 255–61. http://dx.doi.org/10.1080/16742834.2018.1440133.

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Fan, Weiwei, Feng Zhou, Mingliang Tao, et al. "Interference Mitigation for Synthetic Aperture Radar Based on Deep Residual Network." Remote Sensing 11, no. 14 (2019): 1654. http://dx.doi.org/10.3390/rs11141654.

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Radio Frequency Interference (RFI) is a key issue for Synthetic Aperture Radar (SAR) because it can seriously degrade the imaging quality, leading to the misinterpretation of the target scattering characteristics and hindering the subsequent image analysis. To address this issue, we present a narrow-band interference (NBI) and wide-band interference (WBI) mitigation algorithm based on deep residual network (ResNet). First, the short-time Fourier transform (STFT) is used to characterize the interference-corrupted echo in the time–frequency domain. Then, the interference detection model is built by a classical deep convolutional neural network (DCNN) framework to identify whether there is an interference component in the echo. Furthermore, the time–frequency feature of the target signal is extracted and reconstructed by utilizing the ResNet. Finally, the inverse time–frequency Fourier transform (ISTFT) is utilized to transform the time–frequency spectrum of the recovered signal into the time domain. The effectiveness of the interference mitigation algorithm is verified on the simulated and measured SAR data with strip mode and terrain observation by progressive scans (TOPS) mode. Moreover, in comparison with the notch filtering and the eigensubspace filtering, the proposed interference mitigation algorithm can improve the interference mitigation performance, while reducing the computation complexity.

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Ujan, Sahar, Neda Navidi, and Rene Jr Landry. "An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning." Applied Sciences 10, no. 19 (2020): 6885. http://dx.doi.org/10.3390/app10196885.

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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).

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Lim, Sangsoon. "IACR: an interference-aware channel reservation for wireless sensor networks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1220. http://dx.doi.org/10.11591/ijece.v9i2.pp1220-1225.

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<span>In battery-based wireless sensor networks, energy-efficient operation is one of the most important factors. Especially, in order to improve energy efficiency in wireless sensor networks, various studies on low power operation have been actively conducted in the MAC layer. In recent years, mutual interference among various radio technologies using the same radio frequency band has become a serious problem. Wi-Fi, ZigBee, and Bluetooth use the same frequency band of 2.4GHz at the same time, which causes various signal interference problems. In this paper, we propose a novel channel reservation scheme, called IACR, to improve the energy efficiency of wireless sensor networks in an environment where interference occurs between various wireless technologies. The proposed scheme inserts a PN code into a long preamble for exchanging transmission status information between a transmitting node and a receiving node, thereby improving the transmission success probability while receiving less influence on transmission of other radio technologies. We performed an event-driven simulation and an experiment to measure the signal detection rate. As a result, it can be seen that the proposed technique reduces the packet drop rate by 15% and increases the discoverable distance of the control packet for channel reservation.</span>

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Price, D. C., G. Foster, M. Geyer, et al. "A fast radio burst with frequency-dependent polarization detected during Breakthrough Listen observations." Monthly Notices of the Royal Astronomical Society 486, no. 3 (2019): 3636–46. http://dx.doi.org/10.1093/mnras/stz958.

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ABSTRACT Here, we report on the detection and verification of fast radio burst FRB 180301, which occurred on utc 2018 March 1 during the Breakthrough Listen observations with the Parkes telescope. Full-polarization voltage data of the detection were captured – a first for non-repeating FRBs – allowing for coherent de-dispersion and additional verification tests. The coherently de-dispersed dynamic spectrum of FRB 180301 shows complex, polarized frequency structure over a small fractional bandwidth. As FRB 180301 was detected close to the geosynchronous satellite band during a time of known 1–2 GHz satellite transmissions, we consider whether the burst was due to radio interference emitted or reflected from an orbiting object. Based on the pre-ponderance of our verification tests, we cannot conclusively determine FRB 180301 to be either astrophysical or anthropogenic in origin.

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Hamidi, Zety Sharizat, N. N. M. Shariff, and C. Monstein. "Comparison of the Radio Frequency Interference (RFI) in the Region of Solar Burst Type III Data at Selected CALLISTO Network." International Letters of Chemistry, Physics and Astronomy 29 (March 2014): 38–45. http://dx.doi.org/10.18052/www.scipress.com/ilcpa.29.38.

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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.

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ARAI, YOSHIKAZU, KIYOMI IGARASHI, ATSUSHI FUKASAWA, and YUMI TAKIZAWA. "MULTI-USER DETECTION TO ENHANCE THE CAPACITY OF W-CDMA BASED ON THE CONJUGATE GRADIENT METHOD." Journal of Circuits, Systems and Computers 13, no. 02 (2004): 313–23. http://dx.doi.org/10.1142/s0218126604001386.

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Multiple users communicate with individually-assigned codes on a single radio carrier frequency in a DS-CDMA system. Mutual interference among codes limits the capacity of the system. Conventional detectors of receivers are designed for a single-user neglecting the mutual interference. Multi-user detection is expected to enhance the capacity by removing the interference factors. In this paper, a new scheme is proposed corresponding to solving a huge matrix equation under multiple access interference (MAI) environments. This scheme is obtained by the mathematical approach to solve a huge matrix equation. The modeling of multi-user detection is shown, and the algorithm is given based on mathematical approach: the Conjugate Gradient Method (CGM). The characteristics of the proposed scheme are evaluated with comparison to the conventional single-user detector. The proposed scheme has been shown to be three times in capacity against single user detection with reduced complexity comparing conventional technologies based on serial and parallel configurations.

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Musumeci, Luciano, and Fabio Dovis. "Use of the Wavelet Transform for Interference Detection and Mitigation in Global Navigation Satellite Systems." International Journal of Navigation and Observation 2014 (February 26, 2014): 1–14. http://dx.doi.org/10.1155/2014/262186.

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Radio frequency interference detection and mitigation are becoming of paramount importance due to the increasing number of services and applications based on the position obtained by means of Global Navigation Satellite Systems. A way to cope with such threats is the implementation in the receiver of advanced signal processing algorithm able to raise proper warning or improve the receiver performance. In this paper, we propose a method based on the Wavelet Transform able to split the useful signal from the interfering component in a transformed domain. The wavelet packet decomposition and proper statistical thresholds allow the algorithm to show very good performance in case of multiple pulse interference as well as in the case of narrowband interference, two scenarios in which traditional countermeasures might not be effective.

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Deshpande, Avinash A., and B. M. Lewis. "Iridium Satellite Signals: A Case Study in Interference Characterization and Mitigation for Radio Astronomy Observations." Journal of Astronomical Instrumentation 08, no. 01 (2019): 1940009. http://dx.doi.org/10.1142/s2251171719400099.

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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.

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Cosmelli, C., F. Sciamanna, M. G. Castellano, et al. "Stroboscopic single-shot detection of the flux state in a radio frequency superconducting quantum interference device." Applied Physics Letters 80, no. 17 (2002): 3150–52. http://dx.doi.org/10.1063/1.1474605.

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