A radio-frequency interference detector for GNSS navigation and GNSS-reflectometry applications

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.

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.

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 dynamic distribution and change of terrestrial water, manifested in wetlands that support a wide range of vegetation types and ecosystems, and also in floodplains that are prone to inundations, are very important to the understanding of our changing climate and our ability to mitigate risk. At present all of the existing measurement techniques have serious limitations in the ability to observe terrestrial water bodies globally and at the spatial and temporal scales required to fully capture their dynamics. In the last few years a number of studies in the community of Global Navigation Satellite Systems (GNSS) Reflectometry have been focusing on reflections over wetlands and inundated areas [Zuffada et al, 2016; Nghiem et al, 2017; Chew et al., 2018; Morris et al., 2019], particularly since data analysis of the CYclone GNSS (CYGNSS) mission began showing the ability to resolve small-scale land features such as rivers and bodies of water even partially obstructed by vegetation. CYGNSS is a constellation of 8 microsatellites, that provides significantly increased temporal sampling and revisit rate in the tropical latitudinal band as compared to traditional monolithic instruments, thus enabling a new observing strategy for capturing dynamic water events. In [Zuffada et al., 2017; Nghiem et al., 2017], based on reflected signal characteristics such as peaked (limited spread in delay and doppler) and symmetric shape, and very high reflected peak power, it was hypothesized that over wetlands there are strong coherent specular reflections in the collection area of the signal, originating from (even small) areas of standing water, resulting in the measurements’ magnified sensitivity to water because of its high electric permittivity compared to dry land and/or vegetation. Plots of peak power, corresponding to CYGNSS measurements’ specular points, aggregated over a period of time, and displayed over large regions with complex hydrology such as the Amazon basin clearly showed the potential of CYGNSS to map surface hydrology of intricate scenes at the continental level. At the regional scale, availability of in-situ and other correlative data have led to introduce thresholds in peak power values that differentiate between two binary states, i.e. dry and wet associated with inundations [Chew et al., 2018; Morris et al., 2019]. The limitations of the assumptions used to map wetlands have been analyzed in [Loria et al., 2019 (in preparation)] and shown to be traceable to the complex nature of the scattering from inhomogeneous scenes where the local water topology, surface topography and local meteorology can affect the mix of coherent and incoherent scattering, thus producing highly variable peak power that confounds the measurements. This communication summarizes our best understanding of the retrieval accuracy of GNSS reflectometry for dynamic monitoring of wetlands and inundations. It is based on analysis of actual CYGNSS constellation data acquired over terrestrial water bodiesand discusses the limitations in estimating surface water globally when inhomogeneities at the small scales are at play. Comparisons and cross-validations have been performed with measurements from ALOS-2 and Sentinel-1 data. References Chew, C., Reager, J.T. and Small, E., 2018. CYGNSS data map flood inundation during the 2017 Atlantic hurricane season. Scientific Reports (Nature Publisher Group), 8, pp.1-8. Loria, E. et al. Analysis of Wetland Extent Retrieval Accuracy Using CYGNSS Data. In preparation, 2019. Morris, M., C. Chew, J.T. Reager, R. Shah, C. Zuffada. A Novel Approach to Monitoring Wetland Dynamics using CYGNSS: Everglades Case Study. Remote Sensing of Environment, in press. 2019. Nghiem SV, Zuffada C, Shah R, Chew C, Lowe ST, Mannucci AJ, Cardellach E, Brakenridge GR, Geller G, Rosenqvist A. Wetland monitoring with Global Navigation Satellite System reflectometry. Earth and Space Science. 2017 Jan 1;4(1):16-39. Zuffada, C., Chew, C., Nghiem, S.V., Shah, R., Podest, E., Bloom, A.A., Koning, A., Small, E., Schimel, D., Reager, J.T. and Mannucci, A., 2016, August. Advancing Wetlands Mapping and Monitoring with GNSS Reflectometry. In Living Planet Symposium (Vol. 740, p. 83). Zuffada, C., Chew, C. and Nghiem, S.V., 2017. GNSS-R algorithms for wetlands observations. In IGARSS 2017-2017 IEEE International Geoscience and Remote Sensing Symposium (pp. 1126 – 1129), Fort Worth, TX.

In the recent years, the threat of radio frequency interference (RFI) to Global Navigation Satellite Systems (GNSS) receivers has been increasing due to an escalating number of RFI related GNSS issues, and enhanced complexity of jammers and spoofers. This poses significant integrity threats to various systems that depend on GNSS measurements for navigation and guidance. One of the most versatile methods to detect such instances of spoofing is the use of Automatic Gain Control (AGC) measurements provided by GNSS receivers. AGC is indicative of the change in thermal noise levels observed by the frontend of the receiver, thus it can be used to detect presence of RFI. If AGC is coupled with the carrier to noise ratio density (??/????) values of visible GNSS satellites, it is possible to gain insight into which specific constellations or satellites may have been spoofed. Although AGC has been available in some commercial GNSS receivers and customized frontends for some time, the introduction of raw GNSS measurements in Android 7 has recently allowed it to be accessible in some smartphones as well. This study looks at AGC measurements from multiple smartphone models which have different GNSS chipsets, assesses their behavior under RFI, points out the current limitations, and proposes future improvements that would assist in its usage as a GNSS RFI indicator. Furthermore, the paper discusses the future of AGC measurements that would assist in broadening their applications to all GNSS receivers.

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.

Raw Global Navigation Satellites Systems (GNSS) data have been directly accessible from mass-market devices running the Android Nougat (or newer) operating system since late 2016. The availability of GNSS raw data made possible to investigate feasibility of using in-built GNSS chipsets within smartphone devices as passive radar receivers for the purpose of land remote sensing. In this study, we integrate smart-phones into small Unmanned Aircraft Systems (UAS) to collect reflected GNSS raw data for the purpose of mapping top 5-cm soil moisture. The reflected GNSS signals collected by the smartphones show high correlation with spatial features on the ground such as ponds, crops, and small creeks. To determine the quality of smartphone in-built antenna and chipset, we conducted several experiments. The results show that (1) the radiation pattern of smartphone's GNSS antenna are observed to be highly irregular, but time-invariant, and (2) internal GNSS chip produces observables of sufficient quality when the GNSS smartphone reflected signals are compared with a high quality custom-built dual channel receiver. This paper summarizes the experimental findings and challenges that need to be resolved in order to use the GNSS-Reflectometry (GNSS-R) technique via ubiquitous smartphones from small UASs.

The vigorous development of the global navigation satellite system (GNSS) has led to a boom in GNSS radio occultation (GNSS RO) and GNSS reflectometry (GNSS-R) techniques. Consequently, we have proposed an innovative signal processing scheme for spaceborne integrated GNSS remote sensors (SIGRS), combining a GNSS RO and a GNSS-R module. In the SIGRS, the GNSS-R module shares one precise orbit determination (POD) module with the GNSS RO module, and the GNSS-R module first achieves compatibility with GPS, BDS, and Galileo. Moreover, the programmable non-uniform delay resolution was introduced and first used by the SIGRS to generate the output DDM, which achieves a high delay resolution in the DDM central region around the specular point to improve the accuracy of basic observables but requires fewer delay bins than the conventional DDM with uniform delay resolution. The SIGRS has been successfully used to design the GNOS II onboard the Chinese FY-3E satellite, and the results of in-orbit operation validate the performance of the SIGRS, which means the SIGRS is an economically and technically efficient design and has become the first successful signal processing scheme for spaceborne integrated GNSS remote sensors around the world.

Global Navigation Satellite System (GNSS) Radio Occultation measurements can remote sensing Earth's neutral atmosphere and ionosphere. Meanwhile, another GNSS remote sensing technique named GNSS Reflectometry (GNSS-R) has been developed rapidly, which can retrieve sea surface wind speed. The GNSS Occultation Sounder (GNOS) II instrument has been designed to integrate these two GNSS remote sensing techniques in one payload of the third batch FengYun-3 (FY-3) satellites. This paper presents the advancements in research of GNOS II and instrument performance. The qualified module of GNOS II has been manufactured by National Space Science Center (NSSC), Chinese Academy of Sciences. We show that the phase center variety is low enough for the new designed precise orbit determination (POD) antenna to increase the accuracy of precise orbit determination. A wide coverage atmosphere occultation antenna and a 65nm technics ASIC are used in the new generation GNOS instrument. As an important improvement, the GNOS II can generate Delay Doppler Maps (DDMs) of GNSS signals scattered from the ocean surface. The result of test shows that the GNOS II instrument has high performance in precision of code, carrier phase and real-time navigation.

This paper presents a concept, “Cookie,” for a satellite particularly suited for dense spatial sampling by future Global Navigation Satellite Systems (GNSS) remote sensing constellations. Solely based on the reception of the direct and reflected signals transmitted by the GNSS, the satellite can provide observations of the Earth useful for a wide range of scientific and operational applications. The Cookie is capable of receiving direct and reflected signals, in both right- and left-hand circular polarizations, from any of the GNSS systems, and from virtually any arrival direction in both the upper and lower field-of-view hemispheres, i.e., providing nearly full 4π spherical coverage toward any navigation satellite not eclipsed by the Earth. The on-board remote sensing payload produces interferometric observables, e.g., the auto- and cross-correlation of several adequate combinations of the received signals. The interferometric processing is general, the same, and transparent, with respect to any of the signals transmitted by the current or planned GNSS systems. The instrument can implement any other suitable processing schemes too. Such payload can provide, in parallel, accurate GNSS Radio-Occultation (GNSS-RO) observations of the atmosphere and forward and backward GNSS Reflectometry (GNSS-R) measurements from the Earth surface. Several Cookies can be piled up inside the fairing of a rocket minimizing the launch cost of a constellation. A constellation of three Cookies has been simulated and its sampling performance characterized. The key concepts of the Cookie payload could be demonstrated through ESA's GNSS Reflectometry, Radio-Occultation, and Scatterometry experiment on board the International Space Station (GEROS-ISS).

Snow storage dynamics is essential to predict floods, to quantify water resources for human use and irrigation, and to assess the risk of avalanches. Recently, Global Navigation Satellite System (GNSS) ground stations have been successfully used to continuously estimate snow depth at an intermediate scale of about 1.000 $\text{m}^2$ around the stations. In this study, GNSS signal-to-noise ratio (SNR) data at the station Wettzell, Germany, are used to estimate snow depth variations from 2012 to 2015. The station Wettzell is located in a built-up area. The most challenging task at this site is to separate the GNSS reflections from the ground and from surrounding buildings. We modified the interference approach previously used for snow depth estimation using the phase of the multipath interference pattern instead of their frequency. Additionally, we complemented the analysis by including satellites transmitting the L2P signal into the processing. We studied the performance of the L1 signal. The derived GNSS snow depth ranges between 3 and 25 cm and corresponds well to in situ observations by an ultrasonic sensor, with a correlation of 0.8 for daily time series. The residuals of GNSS snow depths compared to the ultrasonic sensor reveal a root-mean-squared error (RMSE) of 4.3 cm for the L2 and 5.9 cm for the L1 signal with a small bias of 1 cm. The results show that the existing data of the global GNSS tracking network promise to provide valuable complementary snow depth observations to the existing sensors at several hundred sites worldwide, including urban areas.

This study performs the first multiclass sea ice classification using global navigation satellite system (GNSS) reflectometry (GNSS-R) data from the Fengyun-3E (FY-3E) satellite. A principal component analysis (PCA) based denoising scheme for GNSS-R delay Doppler maps (DDMs) is proposed. The newly proposed processing methods are applied to FY-3E satellite tracks to retain the most significant features of the data. After removing noise from the DDMs using PCA, DDM classification features are calculated using methods established in the literature. An extremely randomized trees (ET) machine learning (ML) classifier was used to classify the sea ice using the denoised classification observables. The classification results are validated using labels derived from National Snow and Ice Data Center (NSIDC) sea ice concentration data. The output classification accuracy was 87.14% with a 0.7834 Kappa coefficient. Per class F1 scores were 89.72%, 85.53%, and 81.34% for first-year ice (FYI), multi-year ice (MYI), and thin ice (TI), respectively. The proposed method produced improvements across all performance metrics compared to classification without PCA based observables and denoising.

This paper investigates weak tsunami detection using noisy sea surface height (SSH) measurement data such as that recorded by a satellite-borne receiver and produced by the Global Navigation Satellite System (GNSS) reflectometry (GNSS-R) technique. By studying the patterns of many real tsunamis, a triangle function is proposed to model the shape of tsunami lead waves for theoretical studies. Very similar simulation results are produced when the modeled and real tsunami data are used separately, indicating good modeling accuracy. A bin averaging (BA) technique is proposed for detecting a tsunami, and a hypothesis testing method is developed to decide whether a tsunami is present by examining the BA outputs against a predefined threshold. Mathematical formulas are derived for the probability of detection (PD) and the probability of false alarm (PFA), which can be employed as a guideline in parameter selection and performance evaluation. Simulation results using both modeled and real tsunami data demonstrate that, given a PFA of 10%, the PD can be around 60% when the wave height is 45 cm, and the SSH measurement error standard deviation (STD) is 76 cm. The results also show that, when a suitable bin length is selected, a two-stage hypothesis testing scheme and a one stage one produce very similar results. Based on the methods developed here and elsewhere, the GNSS-R technique has the potential for future tsunami detection.

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.

It is well known that the presence of unintentional or intentional Radio Frequency Interference (RFI) signals in the Global Navigation Satellite Systems (GNSS) frequency bands can cause severe positioning performance degradation and even a complete service unavailability. Moreover, with the development of Software Defined Radio (SDR) GNSS signal generators and the popularization of low-cost SDR front-ends, the spoofing of the GNSS civil service turns out to be a real threat for GNSS receivers. Complementary to time and frequency-domain mit-igation techniques, antenna-array based receivers can benefit from spatial domain processing. An antenna array receiver can also discriminate between legitimate GNSS signals from these being broadcasted by the spoofer. In this work, we present an evolution of a receiver-independent GNSS smart antenna architecture for a real-time, automatic and autonomous, simultaneous anti-jamming and anti-spoofing spatial filtering for GNSS bands. The novel spoofer detection and mitigation algorithm is able to detect the spoofer and protect the receiver even in a scenario where the spoofer is transmitted in combination with a strong jammer to force the receiver to lose the legitimate GNSS signal lock. A prototype is also presented, implemented using Commercial Off The Shelf (COTS) components. The proposed smart antenna can be connected to any conventional single-antenna GNSS receiver. The paper includes both the theory of operation, the implementation details and its performance analysis in an extended measurement campaign.