Signal-Level RFI Localization for Synthetic Aperture Interferometric 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.

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 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.

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 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.

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) 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.

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.

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

We investigate the effectiveness of the statistical radio frequency interference (RFI) mitigation technique spectral kurtosis () in the face of simulated realistic RFI signals. estimates the kurtosis of a collection of M power values in a single channel and provides a detection metric that is able to discern between human-made RFI and incoherent astronomical signals of interest. We test the ability of to flag signals with various representative modulation types, data rates, duty cycles, and carrier frequencies. We flag with various accumulation lengths M and implement multiscale , which combines information from adjacent time-frequency bins to mitigate weaknesses in single-scale . We find that signals with significant sidelobe emission from high data rates are harder to flag, as well as signals with a 50% effective duty cycle and weak signal-to-noise ratios. Multiscale with at least one extra channel can detect both the center channel and sideband interference, flagging greater than 90% as long as the bin channel width is wider in frequency than the RFI.

Brazilian Solar Spectroscope (BSS) carry out high time (0.01–0.1 s) resolution solar spectral investigations within the frequency range 1–2.5 GHz on a daily basis. At the moment, a new site is imposed to this facility. This site change became necessary due to both factors: the growing level of radio frequency interference (RFI) signals at actual site and requirements of rising up a new building at the BSS antenna location. We present results of RFI signals search at INPE-Cachoeira Paulista area as purpose to define a new BSS site.

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.

A new type of microwave radiometer detector has been developed that is capable of identifying low level Radio Frequency Interference (RFI) and of reducing or eliminating its effect on the measured brightness temperature. The Agile Digital Detector (ADD) can discriminate between RFI and natural thermal emission signals by directly measuring higher order moments of the signal than the variance that is traditionally measured. After detection, the ADD then uses spectral and temporal filtering methods to selectively remove the RFI. ADD performance has been experimentally verified and its performance characterized while connected to an airborne C-Band radiometer (the NOAA/ETL PSR) installed on a NASA WB-57 flying over major urban centers. Index Terms—microwave radiometer, radio frequency interference

The search for radio technosignatures is an anomaly detection problem: Candidate signals represent needles of interest in the proverbial haystack of radio-frequency interference (RFI). Current search frameworks find an enormity of false-positive signals, especially in large surveys, requiring manual follow-up to a sometimes prohibitive degree. Unsupervised learning provides an algorithmic way to winnow the most anomalous signals from the chaff, as well as group together RFI signals that bear morphological similarities. We present Grouping Low-frequency Observations By Unsupervised Learning After Reduction (GLOBULAR) clustering, a signal processing method that uses hierarchical density-based spatial clustering of applications with noise (or HDBSCAN) to reduce the false-positive rate and isolate outlier signals for further analysis. When combined with a standard narrowband signal detection and spatial filtering pipeline, such as turboSETI, GLOBULAR clustering offers significant improvements in the false-positive rate over the standard pipeline alone, suggesting dramatic potential for the amelioration of manual follow-up requirements for future large surveys. By removing RFI signals in regions of high spectral occupancy, GLOBULAR clustering may also enable the detection of signals missed by the standard pipeline. We benchmark our method against the C. Choza et al. turboSETI-only search of 97 nearby galaxies at the L band, demonstrating a false-positive hit reduction rate of 93.1% and a false-positive event reduction rate of 99.3%.

The Aquarius/SAC-D mission operated between August 2011 and June 2015 with the main goal of providing global estimates of sea surface salinity (SSS). It comprised both active and passive microwave sensors operating at L-band to observe the same surface area almost simultaneously. Measurements from both instruments underwent subsequent filtering to mitigate the effect of Radio Frequency Interference (RFI). However, RFI cannot always be detected, especially when the level of interfering signals is comparable to that of the natural surface emission. A study was performed here to assess the amount of missed detection in certain ocean region where low-level interference is a problem. The approach is to estimate the statistics of RFI starting from the acquired antenna temperature samples. The statistics are then used to simulate RFI and study its detection by the RFI filter. The results of this simulation are compared with the RFI actually detected in the acquired samples to provide an estimate of the level of missed detection.