Computationally Efficient Narrowband RFI Mitigation for Pulse Compression Radar

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.

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.

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

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

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.

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.

This paper will discuss the operational impact of radio frequency interference on a space mission. Operations of unmanned spacecraft require reliable communications between controllers on the ground and the spacecraft they operate. Interference in the radio spectrum can affect not only the ability of operators to send commands to a spacecraft, but also the reliable return of information from the spacecraft payload. Unfortunately, it is not always easy to identify the source of radio frequency interference or to properly plan for service interruptions. The ESA Integral mission is used as a case study to demonstrate the impact of radio frequency interference. Several short duration service interruptions were impacting operations each week. Using the primary ground station located in Kiruna, Sweden, operational systems were developed to better monitor and characterize the source of radio signals. Statistics were derived from the data collected which were used to find ways of reducing the impact of radio frequency interference on operations. This paper will explore techniques which can be used to record radio frequency interference events, identify the source of interference, model future events, and ultimately to better plan mission operations.

: Adaptive RF interference reduction for broadband communication systems continues to be problematic. The acousto-optic RF signal excision system offers real-time, narrowband RF interference, frequency domain filtering for broadband communication systems, but has been limited in its capability for adaptive processing and rejection of broadband RF interference. As a means to reduce these limitations, this dissertation examines the application of a novel photorefractive optical power limiting device to achieve adaptive notch filtering, and multi- channel acousto-optic deflection to achieve angle-of-arrival signal discrimination at the notch filter. This dissertation describes basic principles of acousto-optic RF signal excision, including linear phased- array antenna, angle-of- arrival processing using a multi-channel acousto-optic deflector. The principles are verified experimentally by demonstration of frequency domain filtering according to frequency and angle of arrival. This dissertation also describes basic principles of photorefractive optical power limiting, which is a new approach for achieving adaptive, frequency-domain notch filtering. Photorefractive field shielding of electro-optic birefringence is examined by numerical solution of the band- transport model, and experimental measurement of notch filter amplitude and spatial profiles for high-intensity, Gaussian beam optical illumination and high- intensity, externally applied electric fields. Results indicate that for illumination-beam radius larger than a few microns, power-limiting notch widths follow the illumination intensity and terminate at the dark-to-light illumination interface where excess charge accumulates.

This paper assesses radio frequency interference (RFI) detection and crowdsourced RFI geolocation on modern Android smartphones. Three indicators for RFI are investigated: the number of satellites used in the positioning solution (#SV), automatic gain control (AGC), and carrier-to-noise density ratio (C/N0). These are examined across seven different phones in a total of 13 different tests, including four outdoor real-world benchmarking tests, and nine jamming tests performed inside an anechoic chamber with a small consumer off-the-shelf (COTS) jammer acquired on the internet. The results of these tests, and the behavior of each of the three indicators is presented and analyzed to show some of the limitations and issues that will naturally be involve in using smartphones for this purpose. Such issues include the limited abilities of the GNSS hardware in smartphones, and the dynamical nature of ordinary smartphone usage. Based on these tests a new and reliable RFI detection method is proposed and tested on the available data. This method relies on both the AGC and C/N0 indicators to compute a reliable RFI detector which registers all events in all devices in the anechoic chamber, and has no false detections in the benchmarking tests, in which no RFI is present. Finally, a method for geolocating RFI using crowdsourced smartphone data is presented and assessed. The assessment is done through simulation and gives an indication of the number of smartphones necessary to accurately location a jammer or a similar RFI source.

We present a multimoment technique for signal classification and apply it to the detection of fast radio transients in incoherently dedispersed data. Specifically, we define a spectral modulation index in terms of the fractional variation in intensity across a spectrum. A signal whose intensity is distributed evenly across the entire band has a much lower modulation index than a spectrum with the same intensity localized in a single channel. We are interested in broadband pulses and use the modulation index to excise narrowband radio frequency interference (RFI) by applying a modulation index threshold above which candidate events are removed. The technique is tested both with simulations and using data from sources of known radio pulses (RRAT J1928+15 and giant pulses from the Crab pulsar). We find that our technique is effective at eliminating not only narrowband RFI but also spurious signals from bright, real pulses that are dedispersed at incorrect dispersion measures. The method is generalized to coherent dedispersion, image cubes, and astrophysical narrowband signals that are steady in time. We suggest that the modulation index, along with other statistics using higher-order moments, should be incorporated into signal detection pipelines to characterize and classify 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.

An RFI investigation for setting up a VLBI station below 2.8 GHz in Malaysia

A method to identify and mitigate radio frequency interference (RFI) in microwave radiometry based on the use of a spectrum analyzer has been developed. This method has been tested with CAROLS L-band airborne radiometer data that are strongly corrupted by RFI. RFI is a major limiting factor in passive microwave remote sensing interpretation. Although the 1.400–1.427 GHz bandwidth is protected, RFI sources close to these frequencies are still capable of corrupting radiometric measurements. In order to reduce the detrimental effects of RFI on brightness temperature measurements, a new spectrum analyzer has been added to the CAROLS radiometer system. A post processing algorithm is proposed, based on selective filters within the useful bandwidth divided into sub-bands. Two discriminant analyses based on the computation of kurtosis and Euclidian distances have been compared evaluated and validated in order to accurately separate the RF interference from natural signals.