Detection of RFI by its Amplitude Probability Distribution

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.

Microwave radiometers detecting geophysical parameters are very susceptible to radio-frequency interference (RFI) from anthropogenic sources. RFI is always additive to a brightness observation, and so the presence of RFI can bias geophysical parameter retrieval. As microwave radiometers typically have the most sensitive receivers operating in their band, low-level RFI is both significant and difficult to identify. The kurtosis statistic can be a powerful means of identifying some types of low-level RFI, as thermal noise has a distinct kurtosis value of three, whereas thermal noise contaminated even with low-level nonthermal RFI often has other values of kurtosis. This paper derives some benign distortions of the kurtosis statistic due to digitization effects and demonstrates these effects with a laboratory experiment in which a known amount of low-level RFI is injected into a digital microwave radiometer.

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.

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 view of the inconsistency of channel gains and a large amount of interference noise in Solar Broadband Radio Spectrometer (SBRS) observation data, they will seriously affect the analysis of SBRS data. In this paper, a method of Radio Frequency Interference (RFI) detection and mitigation for SBRS observation data is reported. Firstly, the SBRS observation data are preprocessed, a part of the observation data was selected to calculate the mean and variance to achieve the normalization of the entire observation data, which can avoid the influence of strong noise on the normalization result. Furthermore, we proposed an adaptive threshold RFI detection method based on fusion wavelet transform reconstruction and an RFI elimination method based on neighborhood weighted filling. It is worth mentioning that to detect RFI interference signals of different magnitudes, we adopted an iterative approach to the RFI detection and mitigation process. Through qualitative analysis of real observation data and quantitative analysis of simulated data, it is shown that the method proposed in this paper can effectively eliminate RFI in SBRS observation data, and improve the quality of observation data for further scientific analysis.

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.

Radio Frequency Interference (RFI) is a growing threat to all Earth Observation (EO) passive microwave missions. Many RFI detection algorithms are used on-board and in the ground segment data processing, but there is no single algorithm that can detect all RFI instances. The best strategy is always to combine several detection methods. This paper presents the results of the new Ground RFI Detection System (GRDS). The GRDS uses a combination of a wide variety of RFI detection algorithms to clean the Earth Observation measurements from RFI. The system is built to be able to scan for RFI for any EO mission. The initial results show the important reduction on RFI in the EO data as measured by the European Center for Mid-Range Weather Forecast (ECMWF) first guess departure statistics.

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.

Radio frequency interference (RFI) is a risk for microwave radiometers due to their requirement of very high sensitivity. The Soil Moisture Active Passive (SMAP) mission has an aggressive approach to RFI detection and filtering using dedicated spaceflight hardware and ground processing software. As more sensors push to observe at larger bandwidths in unprotected or shared spectrum, RFI detection continues to be essential. This article presents a deep learning approach to RFI detection using SMAP spectrogram data as input images. The study utilizes the benefits of transfer learning to evaluate the viability of this method for RFI detection in microwave radiometers. The well-known pretrained convolutional neural networks, AlexNet, GoogleNet, and ResNet-101 were investigated. ResNet-101 provided the highest accuracy with respect to validation data (99%), while AlexNet exhibited the highest agreement with SMAP detection (92%).

Characteristics of L-band radio frequency interference detected via the soil moisture active passive radiometer in China and its offshore areas

Microwave Imager Combined Active and Passive (MICAP), which is a package of active and passive microwave instruments including L/C/K-band radiometers and L-band scatterometer, has been approved to be taken onbord the Chinese Ocean Salinity Mission. The L-band one-dimensional synthetic aperture radiometer (L-Rad) is the key part of MICAP to measure sea surface salinity (SSS). Since radio frequency interference (RFI) is reported as a serious threat to L-band radiometry, the RFI detection and mitigation techniques must be carefully designed before launch. However, these techniques need to be developed based on the knowledge of how RFI affects complex correlation, visibility function, and reconstructed brightness temperature. This paper presents a time-domain signal modeling method for the simulation of interferometric measurement under RFI’s presences, and a simulation system for L-Rad is established accordingly. Several RFI cases are simulated with different RFI types, parameters, and positions; and the RFI characteristics upon L-Rad’s measurement are discussed. The proposed simulation system will be further dedicated to the design of RFI processing strategy onboard MICAP.

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.

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.

Shielding techniques are widely applied in suppression of near electric field interference from power converters, so as to enhance their electromagnetic compatibility (EMC) performance. However, sometimes traditional shielding method cannot effectively suppress the electromagnetic interference (EMI) in the full frequency range. In order to make a power converter meet the limit requirement of EMC standard, this paper proposes an improved non-ideal electric field shielding method to balance the suppression effects of the low frequency and high frequency interference. A resistor with appropriate resistance is connected between the shielding layer and the reference ground so as to balance the suppression effect of the high frequency and the low frequency interference. The EMI measurement results show that the power converter with improved shielding method had good EMC performance.

Aquarius is an L-band active/passive sensor designed to globally map sea surface salinity from space [1, 2]. Two instruments, a radar scatterometer and a radiometer, observe the same surface footprint almost simultaneously. The radiometer is the primary instrument for sensing sea surface salinity (SSS), while the scatterometer is included to provide a correction for sea surface roughness, which is a primary source of error in the salinity retrieval. Although the primary objective is the measurement of SSS, the instrument combination operates continuously, acquiring data over land and sea ice as well. An important feature of the data processing includes detection and mitigation of Radio Frequency Interference (RFI), which is done separately for both active and passive instruments. Correcting for RFI is particularly critical over ocean because of the high accuracy required in the brightness temperature measurements for SSS retrieval. It is also necessary for applications of the Aquarius data over land, where man-made interference is widespread, even though less accuracy is required in this case. This paper will provide an overview of the current status of the Aquarius RFI processing and an update on the ongoing work on the improvement of the RFI detection and mitigation performance.