Radio frequency interference and the impact on space operations

Abstract. C-band weather radar data are commonly compromised by interference from external sources even though weather radars are the primary and therefore privileged user of this frequency band. This is also the case for the radar network of the German Meteorological Service (Deutscher Wetterdienst, DWD). Theoretically, dynamic frequency selection (DFS) by devices operating in the C band should prevent any disturbance of the primary user. In practice, this does not always work as intended under the current regulations. As it is not possible to separate a superimposed interference signal from measured weather radar data, the protection of the frequency band is of utmost importance. Currently, the only available option is to discard the compromised portions of the radar data. Therefore, the current best course of action is to shut down radio frequency interference (RFI) sources as quickly as possible. The automated RFI detection algorithm for the German C-band weather radar network, operational since July 2017, makes use of routinely measured radar moments. Built upon data gathered since 2017, an RFI classification with respect to the severity and duration of RFI sources was first implemented in 2019. An independent verification of the RFI detection algorithm was performed by using a commercially available Wi-Fi adapter, which is directly integrated into the radar receiver. Subsequently, a mitigation workflow was implemented to efficiently identify and shut down detected RFI sources by the German Federal Network Agency (Bundesnetzagentur, BNetzA). By following this workflow with great effort, the number of persistent RFI sources has been decreasing since October 2019, while a steady increase in short-lived RFI sources over the last 5 years also exists. In total, 11 889 RFI sources have been identified from July 2017 to May 2022. Most of these (94.8 %) are such short-lived sources that an unambiguous identification by the BNetzA is, in general, not feasible. However, as stated by the C-band regulations, any non-compliant transmitter compromising the operation of a weather radar has to be shut down. This is important, as even these short-lived RFI sources negatively affect meteorological product generation.

Geosynchronous Synthetic Aperture Radar (GEO SAR) is a possible next generation SAR system, which has the excellent performance of less than one-day revisit and hundreds of kilometres coverage. However, Radio Frequency Interference (RFI) is a serious problem, because the specified primary allocation frequencies are shared by the increasing number of microwave devices. More seriously, as the high orbit of GEO SAR makes the system have a very large imaging swath, the RFI signals all over the illuminated continent will interfere and deteriorate the GEO SAR signal. Aimed at the RFI impact in GEO SAR case, this paper focuses on the performance evaluation and the system design requirement of GEO SAR in the presence of RFI impact. Under the RFI impact, Signal-to-Interference-plus-Noise Ratio (SINR) and the required power are theoretically deduced both for the ground RFI and the bistatic scattering RFI cases. Based on the theoretical analysis, performance evaluations of the GEO SAR design examples in the presence of RFI are conducted. The results show that higher RFI intensity and lower working frequency will make the GEO SAR have a higher power requirement for compensating the RFI impact. Moreover, specular RFI bistatic scattering will give rise to the extremely serious impact on GEO SAR, which needs incredible power requirements for compensations. At last, real RFI signal behaviours and statistical analyses based on the SMOS satellite, Beidou-2 navigation satellite and Sentinel-1 A data have been given in the appendix.

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.

Activity recognition is an important component of many pervasive computing applications. Device-free activity recognition has the advantage that it does not have the privacy concern of using cameras and the subjects do not have to carry a device on them. Recently, it has been shown that channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We conduct experiments in environments without and with RFI. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments shows that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. Our evaluation shows the proposed method can improve up to 10% true detection rate in the presence of RFI. We also study the impact of bandwidth on activity recognition performance. We show that with a channel bandwidth of 20 MHz (which is used by WiFi), it is possible to achieve a good activity recognition accuracy when RFI is present.

Mitigating in-band, continuous-wave (CW) radio frequency interference (RFI) is a common problem for radar systems which themselves may employ continuous-wave waveforms. The source of this RFI can be intentional or unintentional spectral artifacts of near-by narrowband communications systems. Radar signal modulations of just a few megahertz are frequently quite broad compared to the modulation bandwidths of the interferers. So, characterization of RFI by a simple tone can be sufficient to assess its impact. When the RFI sources are unanticipated or of uncertain characteristics, there is motivation to mitigate them in a responsive manner in receive processing. A computationally efficient RFI cancellation method for receive is described here. Salient properties of this method are the recovery of radar sensitivity in the presence of narrowband RFI and the preservation of pulse compression time sidelobe levels. In fact, the technique described here overcomes the disadvantages of isolating and removing an RFI source that is completely immersed in the radar waveform in both time and frequency. While removing each of possibly multiple RFI sources with surgical IIR filters, little secondary influences on pulse compression responses arise because the IIR filter is suitably designed with finite word influences and impulse-response time sidelobes in mind.

Radio frequency interference (RFI) has historically plagued radio astronomy, worsening with the rapid spread of electronics and increasing telescope sensitivity. We present a multi-dimensional probabilistic framework for characterizing the RFI environment around a radio astronomy site that uses data from the telescope observations themselves. We apply the framework to about 1500 h of commissioning data (∼200 TB) from the MeerKAT radio telescope; producing a six-dimensional array that yields both average RFI occupancy as well as distributions around the mean as a function of key variables (time of day, frequency, baseline, azimuth, and elevation). This allows for automated alerting for significant new sources of RFI and monitoring of RFI trends. Our results provide the first detailed view of the MeerKAT RFI environment at high sensitivity. As expected, in the MeerKAT L-band, we find that the major RFI contributors are from the global positioning system satellites, flight distance measurement equipment, and the global system for mobile communications. Beyond characterizing RFI environments, our approach allows observers access to the prior probability of RFI in any combination of tracked variables, allowing for more efficient observation planning and data flagging.

Modern infrastructure and a myriad of services rely on positioning and timing information provided by Global Navigation Satellite Systems (GNSS) and in particular the Global Positioning System (GPS). However, given their low received signal power levels, GNSS signals are vulnerable to Radio Frequency Interference (RFI), either from non-intentional or intentional (jamming), sources. Hence, GNSS itself has become a critical infrastructure which must be protected. Since RFI source is unknown a priori, passive localization systems consisting of spatially distributed Sensor Nodes (SNs) are needed to geo-locate the RFI. These systems typically use source Angle of Arrival (AOA), Time Difference of Arrival (TDOA) or a combination of AOA/TDOA measurements which are non-linear in nature, to estimate the RFI position. Also, dynamics associated with the RFI source(s) further complicates the geo-localization process. This paper explores and reports on the use of various Kalman Filters in combining AOA and TDOA measurements for efficient geo-localization and tracking of dynamic and stationary RFI sources based on real measurements from one such geo-localization system. We report on and contrast the geo-localization accuracies and computational complexities of the Extended, Unscented and Single Propagation Unscented Kalman Filters along with the traditional snap-shot approach.

A CNN and LSTM-based approach to classifying transient radio frequency interference

The performance of a cross-frequency detector against pulsed sinusoidal Radio Frequency Interference (RFI) is analyzed theoretically. The effects of duty cycle and RFI strength are examined, and the detection performance of the cross-frequency algorithm is compared with that achieved by pulse and kurtosis detectors. The performance loss that occurs in the cross-frequency algorithm when an RFI source is not centered in a frequency channel is also quantified, and a computationally simple model for this loss is described.

Transport safety systems and electronic security system Personal Rapid Transit (PRT) steering systems constitute a spatially formed set of elements that operate together in order to achieve the assumed level of security at a railway station or during the performance of transportation operations. Occurring intended or unintended electromagnetic interferences in the process of exploitation electronic systems may cause the occurrence of catastrophic damages or can be the reason of a reduction of the work quality of these systems. Small frequency range electromagnetic interference (up to 100 kHz) that occurs in an extensive “railway” area should be considered separately, i.e. the electric and magnetic components of this field should be distinguished. Because of measurements and legal regulations concerning the impact of this interference on elements, devices and electronic systems. Any disruption of the primary functions of the steering system can be the cause of the occurrence of human life and health hazards. The present article discusses the impact of electromagnetic interference: the electric component of the field on the process of the operation of the PRT steering and security system.KeywordsSmall frequencyelectromagnetic interferencesecurity system

The identification and localization of large-range, wide-band electromagnetic interference (EMI) sources have always been both costly and time-consuming. The measurements at different times and places are often required before a typical system can locate a target. In this paper, we proposed a 2D electromagnetic imaging system to localize interference sources and identify the EMI frequency in real time. In this system, an offset paraboloid with a diameter of three meters is designed for large-range EMI imaging, while a multi-channel digital signal acquisition system is developed for wide-band EMI localization. The located interference source is segmented by the maximum entropy method based on particle swarm optimization, and the modified generalized regression neural network (MGRNN) is applied to identify the EMI frequency effectively by excluding misleading effects of outliers. The experiment which has been completed on our dataset indicates that our approach not only increases accuracy by 5% compared with the standard generalized regression neural network approaches for identification, but also exerts a large-range wide-band localization of the EMI source detection method.

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.

The Giant Meterwave Radio Telescope (GMRT) observatory in India consists of 30 antennas in a Y-shaped array, spread out over a 14 km radius from the central processing center. This makes the protection of radio telescopes from increasing radio frequency interference (RFI) at meter wavelengths quite challenging. Radio telescopes are built with broadband receivers to observe beyond the narrow allocated radio astronomy bands, so protecting the receivers from out-ofband emission is a major task. With the rapid increase in mobile communication, TV and FM Radio transmitters, police wireless, industries and power line distribution networks, private and public transport, there is a significant increase in RFI at low frequencies. This paper presents the results of above studies of these external sources of RFI, their effect on the receiver system, and of various methods adopted to minimize the same to protect the GMRT. This paper also describes various control experiments carried out, with measurements made over the past 10 years, and methods adopted to co-exist with commercial communication operators and industries to control the RFI environment at the GMRT The GMRT RFI team has been able to detect and correct 1800 radiating domestic TV boosters spread over a 20 km radius with a simple low cost solution. Using RF and Ultrasonic detectors, dense electrical distribution networks with LT and HT power distribution lines and single phase, 3 phase transformer installations in the neighbor hood are monitored, and steps have been taken to control various power line sources of interference that have affected observations. The GMRT also faces challenges from many GSM and CDMA mobile operators in the region around the GMRT. We have been able to negotiate for a change in operating band and in the orientation of cell towers etc. to protect the GMRT. Efforts are also ongoing to monitor and control the interference from industries operating in nearby regions as well as from internally generated RFI at the GMRT.

STARFIRE: An algorithm for estimating radio frequency interference in orbits around Earth

Currently, frequency-domain electromagnetic interference (EMI) modeling has become the most commonly used method for EMI prediction and filter design. However, because of the unpredictability of the ringing effect in the present frequency-domain EMI modeling methods, the precision of the predicted EMI interference source is notably limited, which results in the inaccuracy of the system level EMI prediction. In order to achieve an accurate EMI prediction, this study presents a systematical study on the interference source of a 10 kVA three-phase bridge converter. The precise mathematical models of the common-mode and differential-mode interference source are constructed. Then, the characteristics of the multiple ringing effect, which is an important part of the interference source, are intensively analyzed. Based on the investigation of interference source, an improved interference source synthesis method is proposed. It can quickly and precisely predict the CM and DM interference source with multiple ringing effect, which can be used to achieve accurate frequency domain EMI prediction. Moreover, the multiple ringing effect is extended for IGBT parasitic parameter measuring. Instead of complicated device-level measurement, the grounding capacitance and junction capacitance can be effectively extracted through the ringing frequency measurement.