Simulating Spectral Kurtosis Mitigation against Realistic Radio Frequency Interference Signals

This paper focuses on the time-variant radio frequency interference (RFI) issue that ultra-wide band (UWB) through-wall radar (TWR) is faced with, and presents an iterative dual sparse recovery (IDSR) framework to combat it. The framework consists of two stages: 1) RFI estimation and detection and 2) IDSR of scattered echoes from objects and RFI signals. In the first stage, an overlapped short time Fourier transform is employed to construct and update the discrete frequency Doppler spectrum (DFDS). Then, a minimum statistic operation is conducted on the DFDS to estimate RFI signals, followed by detection via a 1-D cell-averaging constant false alarm rate detector to determinate whether RFI signals exist or not. In the second stage, a dual sparse model of the collected signals is set up, based on the fast-time frequency sparsity of RFI signals because of their narrow bands and the Doppler frequency sparsity of scattered echoes from objects because of their limited moving velocities. The alternating direction method of multipliers (ADMM) is introduced to iteratively and alternately recover RFI signals and scattered echoes from objects. Specifically, the iterative hard thresholding (IHT) method is used to complete the two sparse recovery operations. The involved two sparse dictionaries are simple and only made up of inverse discrete Fourier transform basis and independent of the received signals. The IDSR framework is able to relieve the influences of RFI signals and scattered echoes from objects on each other, and thus can reconstruct the two type signals to the maximum limit. Field experiments using a UWB TWR were carried out to verify the proposed method.

The Chinese Ocean Salinity Mission (COSM) was successfully launched on November 14, 2024, with the objective of observing global sea surface salinity (SSS). To fulfill this mission, two payloads were onboard, one of which is a key instrument called the microwave imager combined active and passive (MICAP). MICAP comprises L-/C-/K-band 1-D synthetic aperture interferometric radiometers (SAIRs) that share a parabolic reflector, as well as an L-band scatterometer. As evidenced by SMOS, accurately locating low- and mid-level radio frequency interference (RFI) signals for SAIR is challenging due to issues such as relatively low sensitivity and Gibbs oscillations in complex scenarios. Owing to the relatively small number of array elements in 1D-SAIRs and advanced data transmission technology, MICAP has achieved the capability to downlink short-duration array raw sampling data before array signal correlation, providing strong support for identifying low- and mid-level RFI signals in complex scenarios. In this article, a signal-level RFI localization method is proposed. First, the strong RFI signal is eliminated by leveraging the spatial point source characteristics of RFI. Then, low- and mid-level RFI detection and localization are achieved by extracting non-Gaussian RFI signals from Gaussian background signals through the calculation of kurtosis spectra. Simulations demonstrate that the detection rate and localization accuracy for low- and mid-level RFI signals can be significantly improved.

There are more systems than ever reliant on Global Navigation Satellite Systems (GNSS), which results in even more systems than ever being susceptible to Radio Frequency Interference (RFI). Even RFI at low levels can be catastrophic to systems dependent on GNSS. RFI signals can prevent GNSS signals from reaching the user (interference or jamming) or provide the user false signals resulting in an incorrect position and time solution (spoofing). Our effort centers on developing a low-cost GNSS monitor utilizing the u-blox F9 GNSS receiver, an inexpensive commercial receiver offering multi-constellation and dual frequency position and time solutions, as well as many powerful RFI detection metrics. Different receivers and antennas may react differently in RFI scenarios based on both hardware and software configurations, and offer varying RFI rejection techniques and detection metrics to the user. As a result, it is important to gain a good understanding of the receiver's behavior. This begins by doing a thorough analysis of the receiver's performance under nominal conditions. Multiple u-Blox F9 receivers have been deployed with custom software to monitor and record data across the United States for extended periods of time. This paper analyzes the receiver’s baseline response in different nominal environments over thirty continuous days. Several of the receiver’s RFI detection metrics are analyzed and compared between receivers both close and hundreds of miles apart, including automatic gain control (AGC), carrier-to-noise ratio (C/N0), spectral analysis, and built in RFI indicators. Having an in depth understanding of the variance in these metrics under nominal conditions allows for much more accurate and effective RFI detection and characterization by distinguishing nominal changes from more dangerous jamming or spoofing. The analysis of these metrics over an extended period is presented within the paper and demonstrates the value of rigorous data collection/monitoring/analysis enabled through this low-cost sensor network.

To extract the immunity model in an easy way and to complete the immunity simulation in a short time, it is preferred to consider only the disturbance propagation network in an integrated circuit system. However, through theoretical analyses, simulations and measurements, this paper shows that the on-chip transistor circuit has a nonuniform frequency response on its immunity against arrival disturbances. Including the nonuniform frequency response qualitatively improves the match between the simulation and measurement results. The conclusion is that both the disturbance propagation network and on-chip transistor circuit should be considered in the immunity simulation. The evaluation of the electrical performance in the design phase of an electronic system can only be conducted with simulation. Simulating the radio frequency (RF) immunity of integrated circuits (ICs) is interesting in the design phase of the ICs and printed circuit boards. IEC has established a standard for immunity modelling of ICs (1). The frequency of the RF interference (RFI) signal is labeled with fRFI. The RF immunity of ICs is characterized with immunity curves in fRFI range from below 1MHz to near or above 1GHz. The immunity curve shows, for each fRFI, the maximal RFI power (PRFI T) that the IC can tolerate. Normally, a measured RF immunity has the frequency behaviour that PRFI T varies strongly with fRFI. The main task of the RF immunity modeling is to simulate that frequency behaviour. When a RFI signal is applied to an IC, the signal goes through the disturbance propagation network (DPN) and arrives at the on-chip transistor circuit (OCTC). Previous researches (2{7) in immunity modeling of modern ICs have focused on constructing the model of DPN and use the frequency behaviour of DPN to reproduce the frequency behaviour of the RF immunity. However, besides DPN, the response of OCTC to the arrived RFI may vary with fRFI. The relationship between the maximal tolerable RFI voltage which has arrived at OCTC and fRFI is called the intrinsic immunity of OCTC. The motivation of this paper is to check whether the intrinsic immunity of OCTC is, in fRFI range from 1MHz to 1GHz, a constant or not. If the intrinsic immunity is a constant, then modeling the DPN is su-cient to simulate the RF immunity of the IC. Otherwise, the frequency behaviour of the intrinsic immunity of OCTC should be included in simulating the RF immunity of the IC. The latter will complicate the immunity modeling to a greater degree, because simulating a modern IC, no matter the whole chip or a functional module, at transistor level normally requires huge resources. The \Frequency Behaviour of the Immunity section analyses the signal relations in the immunity test flxture, and explains the concepts of immunity and intrinsic immunity. The \simulation section illustrates two approaches of the immunity simulation. One approach considers the intrinsic immunity of OCTC while the other does not. The \Discussion section compares the simulation results with the measurement results. The difierence in the results of the two approaches brings out the conclusion on the importance of the intrinsic immunity.

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.

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.

ABSTRACTA radio frequency interference (RFI) excision algorithm based on spectral kurtosis, a spectral variant of time‐domain kurtosis, is proposed and implemented in software. The algorithm works by providing a robust estimator for Gaussian noise that, when violated, indicates the presence of non‐Gaussian RFI. A theoretical formalism is used that unifies the well‐known time‐domain kurtosis estimator with past work related to spectral kurtosis, and leads naturally to a single expression encompassing both. The algorithm accumulates the first two powers of M power spectral density (PSD) estimates, obtained via Fourier transform, to form a spectral kurtosis (SK) estimator whose expected statistical variance is used to define an RFI detection threshold. The performance of the algorithm is theoretically evaluated for different time‐domain RFI characteristics and signal‐to‐noise ratios η. The theoretical performance of the algorithm for intermittent RFI (RFI present in R out of M PSD estimates) is evaluated and shown to depend greatly on the duty cycle, d = R/M. The algorithm is most effective for d = 1/(4 + η), but cannot distinguish RFI from Gaussian noise at any η when d = 0.5. The expected efficiency and robustness of the algorithm are tested using data from the newly designed FASR Subsystem Testbed radio interferometer operating at the Owens Valley Solar Array. The ability of the algorithm to discriminate RFI against the temporally and spectrally complex radio emission produced during solar radio bursts is demonstrated.

Radio astronomical data is increasingly corrupted by human telecommunication activities. It is becoming particularly difficult to work at frequencies between 1100–1300 MHz that are rapidly filling up with satellite navigation signals. Therefore, Radio Frequency Interference (RFI) mitigation becomes an important step in the data processing flow. As to the telescopes based on Antenna array, RFI mitigation can be conducted in spatial domain using the sampled covariance matrix. In this paper, a subspace tracking RFI approach has been proposed based on the power method. The RFI spatial signature estimations provided by the reference antenna are used to initialize the power method to support a faster convergence and more accurate estimation. In addition, the reference antenna sequentially steers at the multiple interferences, thereby acquires new updates of each RFI spatial signature successively. Then, the Interference can be mitigated individually by applying the orthogonal projection. The simulation results validate the effectiveness of the proposed algorithm.

Satellite microwave emission mixed with signals from active sensors is referred to as radio-frequency interference (RFI). RFI affects greatly the quality of data and retrieval products from space-borne microwave radiometry. An accurate RFI detection will not only enhance geophysical retrievals over land but also provide evidence of the much-needed protection of the microwave frequency band for satellite remote sensing technologies. It is difficult to detect RFI from space-borne microwave radiometer data over winter land, because RFI signals are usually mixed with snow in mid-high latitudes. A modified principal component analysis (PCA) method is proposed in this paper for detecting microwave low frequency RFI signals. Only three original variables, one RFI index (sensitive to RFI signal) and two scattering indices (sensitive to snow scattering), are included in the vector for principal component analysis in this modified method instead of the nine or seven RFI index original variables used in a normal PCA algorithm. The principal component with higher correlation and contribution to the original RFI index is the RFI-related principal component. In the absence of a reliable validation data set of the “true” RFI, the consistency in the identified RFI distribution obtained from this method compared to other independent methods, such as the spectral difference method, the normalized PCA method, and the double PCA method, give confidence to the RFI signals’ identification over land. The simple and reliable modified PCA method could successfully detect RFI not only in summer but also in winter AMSR-E data.

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.

In this paper we propose a scheme for Radio Frequency Interference (RFI) mitigation in Gigabit Digital Subscriber Line (GDSL) systems using Adaptive Complex Narrowband Filter Banks. This method is compared with other schemes for suppressing complex RFI, such as the Frequency Excision Method and Complex RFI Identification and Cancellation Algorithm. The research shows that the different schemes present different performances, depending on the parameters of the Multiple Input Multiple Output (MIMO) DSL system, but an optimal solution is the application of an Adaptive Narrowband Filter Bank which offers a balance between exceptional RFI suppression efficiency and reasonable computational complexity.

Radio-Frequency Interference (RFI) is a growing problem for applications based on the Global Navigation Satellite Systems (GNSS), including Earth observation using GNSS — Reflectometry (GNSS-R). Nowadays, many efforts are being concentrated to develop RFI signal detectors with high sensitivity, which can help to control the proliferation of RFI generators or jammers. This work aims at designing and testing an instrument capable of detecting and localizing RFI signals at GNSS bands using a microwave radiometer architecture, and a combination of statistical and time-frequency digital detection techniques. The instrument has been tested using different kinds of RFI signals, and its performance has been evaluated as a function of the RFI power. A sensitivity of −135 dBm for continuous wave and chirp signals is reported.

Radio Frequency Interference (RFI) can deteriorate imaging quality of low frequency band synthetic aperture radar (SAR). For SAR using Linear Frequency Modulation (LFM) signals, this paper proposes an RFI suppression method based on clutter estimation in range frequency domain. This method uses eigen-decomposition to complete RFI detection and gets the maximum likelihood estimation (MLE) of clutter intensity for imaging scene. After determining spectrum intervals of RFI signals by the judging method of threshold, our method uses wiener filter to filter the RFI signals in the range frequency domain. The processing results of L-band SAR data verify the effectiveness of our method.

A microwave radiometer specifically designed to detect and mitigate many types of Radio Frequency Interference (RFI) is described. The L-band RFI Detection and Mitigation Testbed (DetMit Testbed) will not be optimized for radiometric observation as much as it is optimized for flexibility in the presence of RFI. While the DetMit Testbed will be a fully functional polarimetric L-band radiometer, the ultimate application of this instrument is not so much brightness measurements as it will be validation of RFI mitigation strategies for employment in future L-band (and other frequency) radiometers. The design approaches for the L-band RFI Detection and Mitigation Testbed are expected to apply to C- band and X-band, and presumably also to other frequencies of interest that experience RFI.

Since autumn 2008 a new L-band 7-Feed-Array receiver is used for an HI 21-cm line survey performed with the 100-m Effelsberg telescope. The survey will cover the whole northern hemisphere comprising both, the galactic and extragalactic sky in parallel. Using state-of-the-art FPGA based digital Fast Fourier Transform spectrometers, superior in dynamic range and temporal resolution, allows to apply sophisticated radio frequency interferences (RFI) mitigation schemes to the survey data. The EBHIS data reduction software includes the RFI mitigation, gain-curve correction, intensity calibration, stray-radiation correction, gridding, and source detection. We discuss the severe degradation of radio astronomical HI data by RFI signals and the gain in scientific yield when applying modern RFI mitigation schemes. For this aim simulations of the galaxy distribution within the local volume (z<0.07) with and without RFI degradation were performed. These simulations, allow us to investigate potential biases and selection effects introduced by the data reduction software and the applied source parametrization methods.