GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector

Performance Bounds for Finite Moving Average Change Detection: Application to Global Navigation Satellite Systems

Due to the widespread deployment of Global Navigation Satellite Systems (GNSSs) for critical road or urban applications, one of the major challenges to be solved is the provision of integrity to terrestrial environments, so that GNSS may be safety used in these applications. To do so, the integrity of the received GNSS signal must be analyzed in order to detect some local effect disturbing the received signal. This is desirable because the presence of some local effect may cause large position errors, and hence compromise the signal integrity. Moreover, the detection of such disturbing effects must be done before some pre-established delay. This kind of detection lies within the field of transient change detection. In this work, a finite moving average stopping time is proposed in order to approach the signal integrity problem with a transient change detection framework. The statistical performance of this stopping time is investigated and compared, in the context of multipath detection, to other different methods available in the literature. Numerical results are presented in order to assess their performance.Comment: 12 pages, 2 figures, transaction paper, IEEE Transaction on Signal Processing, 201

A review of RFI mitigation techniques in microwave radiometry

Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version

On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection

In this paper, the Extended Coupled Amplitude Delay Lock Loop (ECADLL) architecture, previously introduced as a solution able to deal with a multipath environment, is revisited and improved to tailor it to spoofing detection purposes. Exploiting a properly-defined decision algorithm, the architecture is able to effectively detect a spoofer attack, as well as distinguish it from other kinds of interference events. The new algorithm is used to classify them according to their characteristics. We also introduce the use of a ratio metric detector in order to reduce the detection latency and the computational load of the architecture

Jamming Detection using Wavelet Transforms

Modern Global Navigation Satellite Systems (GNSS), such as GPS and Galileo, play vital role in providing high precision navigation and positioning services for civilian and military applications. The high precision feature of these systems is compromised in the presence of interference, particularly intentional narrowband interference otherwise commonly known as jamming. To ensure the sustainability of high precision, removal of jamming components is necessary. In order to achieve successful elimination of jamming components, efficient detection and understanding of the nature of jamming signals are vital. In practice, signals are finite in nature and vary over time. Mathematical tools such as Fourier transforms assume signals are infinite (periodic), thereby fail to capture accurate time-related information. To overcome this situation, a sophisticated technique that captures valuable information in both time and frequency domains is required. One such technique is the wavelet transform. Wavelet transform involves successive scaling of fast decaying wavelike oscillations known as wavelets in time and shifting it along the duration of an incoming signal. This process results in either stretching or shrinking of wavelets. Stretched wavelet facilitates the extraction of slow variations in a signal and compressed wavelet facilitates the extraction of abrupt variations. The conceived algorithm detects the presence of jamming signals, simultaneously capturing features such as frequency, bandwidth and duration. The operational capability of the algorithm was tested for GNSS signals operating in L1 frequency band (1575.42MHz) such as GPS L1 and Galileo E1. The parameters defined to measure the efficiency of the algorithm are detection probability (Pd) and false alarm probability (Pfa). Pd is estimated for different values of jammer to signal ratio (JSR) with fixed signal to noise ratio (SNR) and Pfa depends on the choice of detection threshold (T). T is chosen such that Pfa is as low as possible. The detector works better in low noise and high jammer power scenarios. Keywords: Jamming, Wavelets, GPS, Galileo, SNR, JSR, L

Forward and Inverse Modeling of GPS Multipath for Snow Monitoring

Snowpacks provide reservoirs of freshwater, storing solid precipitation and delaying runoff to be released later in the spring and summer when it is most needed. The goal of this dissertation is to develop the technique of GPS multipath reflectometry (GPS-MR) for ground-based measurement of snow depth. The phenomenon of multipath in GPS constitutes the reception of reflected signals in conjunction with the direct signal from a satellite. As these coherent direct and reflected signals go in and out of phase, signal-to-noise ratio (SNR) exhibits peaks and troughs that can be related to land surface characteristics. In contrast to other GPS reflectometry modes, in GPS-MR the poorly separated composite signal is collected utilizing a single antenna and correlated against a single replica. SNR observations derived from the newer L2-frequency civilian GPS signal (L2C) are used, as recorded by commercial off-the-shelf receivers and geodetic-quality antennas in existing GPS sites. I developed a forward/inverse approach for modeling GPS multipath present in SNR observations. The model here is unique in that it capitalizes on known information about the antenna response and the physics of surface scattering to aid in retrieving the unknown snow conditions in the antenna surroundings. This physically-based forward model is utilized to simulate the surface and antenna coupling. The statistically-rigorous inverse model is considered in two parts. Part I (theory) explains how the snow characteristics are parameterized; the observation/parameter sensitivity; inversion errors; and parameter uncertainty, which serves to indicate the sensing footprint where the reflection originates. Part II (practice) applies the multipath model to SNR observations and validates the resulting GPS retrievals against independent in situ measurements during a 1-3 year period in three different environments - grasslands, alpine, and forested. The assessment yields a correlation of 0.98 and an RMS error of 6-8 cm, with the GPS under-estimating in situ snow depth by approximately 15%. GPS daily site averages were found effective in mitigating random noise without unduly smoothing the sharp transitions as captured in new snow events. This work corroborates the readiness of quality-controlled GPS-MR for snow depth monitoring, reinforcing its maturity for operational usage

Anomalien havaitseminen GNSS signaaleissa kompleksiarvoisilla LSTM neuroverkoilla

Today, Global Navigation Satellite Systems (GNSS) provide services that many critical systems [1] as well as normal users, need in everyday life. These signals are threatened by unintentional and intentional interference. The received satellite signals are complex-valued by nature, however, state-of-the-art anomaly detection approaches operate in the real domain. Changing the anomaly detection into the complex domain allows for preserving the phase component of the signal data. In this thesis, I developed and tested a fully complex-valued Long Short-Term Memory (LSTM) based autoencoder for anomaly detection. I also developed a method for scaling of complex-numbers that forces both real and imaginary units into the range [-1,1] and does not change the direction of a complex vector. The model is trained and tested both in the time and frequency domains, and the frequency domain is divided into two parts: real and complex domain. The developed model’s training data consists only of clean sample data, and the output of the model is the reconstruction of the model’s input. In testing, it can be determined whether the output is clean or anomalous based on the reconstruction error and the computed threshold value. The results show that the autoencoder model in the real domain outperforms the model trained in the complex domain. This does not indicate that the anomaly detection in the complex domain does not work; rather, the model’s architecture needs improvements, and the amount of training data must be increased to reduce the overfitting of the complex domain and thus improve the anomaly detection capability. It was also investigated that some anomalous sample sequences contain a few large valued spikes while other values in the same data snapshot are smaller. After scaling, the values other than in the spikes get closer to zero. This phenomenon causes small reconstruction errors in the model and yields false predictions in the complex domain

On the potential of empirical mode decomposition for RFI mitigation in microwave radiometry

Radio-frequency interference (RFI) is an increasing problem particularly for Earth observation using microwave radiometry. RFI has been observed, for example, at L-band by the European Space Agency’s (ESA’s) soil moisture and ocean salinity (SMOS) Earth Explorer and by National Aeronautics and Space Administration’s (NASA’s) soil moisture active passive (SMAP) and Aquarius missions, as well as at C-band by Advanced Microwave Scanning Radiometer (AMSR)-E and AMSR-2, and at 10.7 and 18.7 GHz by AMSR-E, AMSR-2, WindSat, and GPM Microwave Imager (GMI). Therefore, systems dedicated to interference detection and removal of contaminated measurements are nowadays a must in order to improve radiometric accuracy and reduce the loss of spatial coverage caused by interference. In this work, the feasibility of using the empirical mode decomposition (EMD) technique for RFI mitigation is explored. The EMD, also known as Hilbert–Huang transform (HHT), is an algorithm that decomposes the signal into intrinsic mode functions (IMFs). The achieved performance is analyzed, and the opportunities and caveats that this type of methods present are described. EMD is found to be a practical RFI mitigation method, albeit presenting some limitations and considerable complexity. Nevertheless, in some conditions, EMD exhibits a better performance than other commonly used methods (such as frequency binning). In particular, it has been found that EMD performs well for RFI affecting the <25% lower part of the intermediate frequency (IF) bandwidth.This work was supported in part by the Sensing With Pioneering Opportunistic Techniques (SPOT) under Grant RTI2018-099008-B-C21/ AEI/10.13039/501100011033, in part by the RYC-2016-20918 under Grant MCIN/AEI/10.13039/501100011033, and in part by the European Social Fund (ESF), Investing in your future.Peer ReviewedPostprint (author's final draft

Signal processing techniques for GNSS anti-spoofing algorithms

The Global Navigation Satellite Systems (GNSS) usage is growing at a very high rate, and more applications are relying on GNSS for correct functioning. With the introduction of new GNSSs, like the European Galileo and the Chinese Beidou, in addition to the existing ones, the United States Global Positioning System (GPS) and the Russian GLONASS, the applications, accuracy of the position and usage of the signals are increasing by the day. Given that GNSS signals are received with very low power, they are prone to interference events that may reduce the usage or decrease the accuracy. From these interference, the spoofing attack is the one that has drawn major concerns in the GNSS community. A spoofing attack consist on the transmission of GNSS-like signals, with the goal of taking control of the receiver and make it compute an erroneous position and time solution. In the thesis, we focus on the design and validation of different signal processing techniques, that aim at detection and mitigation of the spoofing attack effects. These are standalone techniques, working at the receiver’s level and providing discrimination of spoofing events without the need of external hardware or communication links. Four different techniques are explored, each of them with its unique sets of advantages and disadvantages, and a unique approach to spoofing detection. For these techniques, a spoofing detection algorithm is designed and implemented, and its capabilities are validated by means of a set of datasets containing spoofing signals. The thesis focuses on two different aspects of the techniques, divided as per detection and mitigation capabilities. Both detection techniques are complementary, their joint use is explored and experimental results are shown that demonstrate the advantages. In addition, each mitigation technique is analyzed separately as they require specialized receiver architecture in order to achieve spoofing detection and mitigation. These techniques are able to decrease the effects of the spoofing attacks, to the point of removing the spoofing signal from the receiver and compute navigation solutions that are not controlled by the spoofer and lead in more accurate end results. The main contributions of this thesis are: the description of a multidimensional ratio metric test for distinction between spoofing and multipath effects; the introduction of a cross-check between automatic gain control measurements and the carrier to noise density ratio, for distinction between spoofing attacks and other interference events; the description of a novel signal processing method for detection and mitigation of spoofing effects, based on the use of linear regression algorithms; and the description of a spoofing detection algorithm based on a feedback tracking architecture