Global Navigation Satellite Systems (GNSS's) such as the United States GPS system are used for a variety of applications much more serious than navigating around a city on your cell phone; GNSS signals provide timing information to critical infrastructures including the power grid, transportation, and financial markets, as well as developing areas such as autonomous vehicles. Unfortunately these signals have been show to be "spoof"-able, allowing an attacker to alter the perceived position or time of a GNSS receiver, which depending on the system the receiver supports, can cause effects ranging from shutting off generators at a power plant, to incorrectly sequencing stock market transactions, to causing an autonomous car to track incorrectly. It is even easier to jam the signals and interrupt position or time information. Our work focuses on methods to detect and mitigate effects of signal interference such as jamming and spoofing, with specific emphasis on applications within the Smart Grid. We also work on methods to reduce complexity and power consumption of receivers, improve accuracy of position and time results in commodity receivers such as smartphones, and mitigate natural interference sources.
Abstract: Precision Orbit Determination (POD) is a prerequisite for the success of many Low Earth Orbiting (LEO) satellite missions. With high-quality, dual-frequency Global Positioning System (GPS) receivers, typically precisions of the order of a few cm are possible for single-satellite POD, and of a few mm for relative POD of formation flying spacecraft with baselines up to hundreds of km. To achieve the best precision, the use of Phase Center Variation (PCV) maps is indispensable. For LEO GPS receivers, often a-priori PCV maps are obtained by a pre-launch ground campaign, which is not able to represent the real space-borne environment of satellites. Therefore, in-flight calibration of the GPS antenna is more widely conducted.
Pub.: 24 Mar '17, Pinned: 28 Jun '17
Abstract: It is well known that tsunamis can produce gravity waves that propagate up to the ionosphere generating disturbed electron densities in the E and F regions. These ionospheric disturbances can be studied in detail using ionospheric total electron content (TEC) measurements collected by continuously operating ground-based receivers from the Global Navigation Satellite Systems (GNSS). Here, we present results using a new approach, named VARION (Variometric Approach for Real-Time Ionosphere Observation), and estimate slant TEC (sTEC) variations in a real-time scenario. Using the VARION algorithm we compute TEC variations at 56 GPS receivers in Hawaii as induced by the 2012 Haida Gwaii tsunami event. We observe TEC perturbations with amplitudes of up to 0.25 TEC units and traveling ionospheric perturbations (TIDs) moving away from the earthquake epicenter at an approximate speed of 316 m/s. We perform a wavelet analysis to analyze localized variations of power in the TEC time series and we find perturbation periods consistent with a tsunami typical deep ocean period. Finally, we present comparisons with the real-time tsunami MOST (Method of Splitting Tsunami) model produced by the NOAA Center for Tsunami Research and we observe variations in TEC that correlate in time and space with the tsunami waves.
Pub.: 22 Apr '17, Pinned: 28 Jun '17
Abstract: The rise of autonomous systems operating close to humans imposes new challenges in terms of robustness and precision on the estimation and control algorithms. Approaches based on nonlinear optimization, such as moving horizon estimation, have been shown to improve the accuracy of the estimated solution compared to traditional filter techniques. This paper introduces an optimization-based framework for multi-sensor fusion following a moving horizon scheme. The framework is applied to the often occurring estimation problem of motion tracking by fusing measurements of a global navigation satellite system receiver and an inertial measurement unit. The resulting algorithm is used to estimate position, velocity, and orientation of a maneuvering airplane and is evaluated against an accurate reference trajectory. A detailed study of the influence of the horizon length on the quality of the solution is presented and evaluated against filter-like and batch solutions of the problem. The versatile configuration possibilities of the framework are finally used to analyze the estimated solutions at different evaluation times exposing a nearly linear behavior of the sensor fusion problem.
Pub.: 24 May '17, Pinned: 28 Jun '17
Abstract: Global Navigation Satellite Systems (GNSS) have been widely used in navigation, positioning and timing. Nowadays, the multipath errors may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), i.e., GPS-Multipath Reflectometry (GPS-MR). However, bistatic scattering properties and the relation between GPS observables and geophysical parameters are not clear, e.g., vegetation. In this paper, a new element on bistatic scattering properties of vegetation is incorporated into the traditional GPS-MR model. This new element is the first-order radiative transfer equation model. The new forward GPS multipath simulator is able to explicitly link the vegetation parameters with GPS multipath observables (signal-to-noise-ratio (SNR), code pseudorange and carrier phase observables). The trunk layer and its corresponding scattering mechanisms are ignored since GPS-MR is not suitable for high forest monitoring due to the coherence of direct and reflected signals. Based on this new model, the developed simulator can present how the GPS signals (L1 and L2 carrier frequencies, C/A, P(Y) and L2C modulations) are transmitted (scattered and absorbed) through vegetation medium and received by GPS receivers. Simulation results show that the wheat will decrease the amplitudes of GPS multipath observables (SNR, phase and code), if we increase the vegetation moisture contents or the scatters sizes (stem or leaf). Although the Specular-Ground component dominates the total specular scattering, vegetation covered ground soil moisture has almost no effects on the final multipath signatures. Our simulated results are consistent with previous results for environmental parameter detections by GPS-MR.
Pub.: 08 Jun '17, Pinned: 28 Jun '17
Abstract: The Kalman filter has been widely applied in the field of dynamic navigation and positioning. However, its performance will be degraded in the presence of significant model errors and uncertain interferences. In the literature, the fading filter was proposed to control the influences of the model errors, and the H-infinity filter can be adopted to address the uncertainties by minimizing the estimation error in the worst case. In this paper, a new multiple fading factor, suitable for the Global Positioning System (GPS) and the Inertial Navigation System (INS) integrated navigation system, is proposed based on the optimization of the filter, and a comprehensive filtering algorithm is constructed by integrating the advantages of the H-infinity filter and the proposed multiple fading filter. Measurement data of the GPS/INS integrated navigation system are collected under actual conditions. Stability and robustness of the proposed filtering algorithm are tested with various experiments and contrastive analysis are performed with the measurement data. Results demonstrate that both the filter divergence and the influences of outliers are restrained effectively with the proposed filtering algorithm, and precision of the filtering results are improved simultaneously.
Pub.: 08 Jun '17, Pinned: 28 Jun '17
Abstract: Magnetometers provide compass information, and are widely used for navigation, orientation and alignment of objects. As magnetometers are affected by sensor biases and eventually by systematic distortions of the Earth magnetic field, a calibration is needed. In this paper, a method for calibration of magnetometers with three Global Navigation Satellite System (GNSS) receivers is presented. We perform a least-squares estimation of the magnetic flux and sensor biases using GNSS-based attitude information. The attitude is obtained from the relative positions between the GNSS receivers in the North-East-Down coordinate frame and prior knowledge of these relative positions in the platform's coordinate frame. The relative positions and integer ambiguities of the periodic carrier phase measurements are determined with an integer least-squares estimation using an integer decorrelation and sequential tree search. Prior knowledge on the relative positions is used to increase the success rate of ambiguity fixing. We have validated the proposed method with low-cost magnetometers and GNSS receivers on a vehicle in a test drive. The calibration enabled a consistent heading determination with an accuracy of five degrees. This precise magnetometer-based attitude information allows an instantaneous GNSS integer ambiguity fixing.
Pub.: 09 Jun '17, Pinned: 28 Jun '17
Abstract: We have developed a suite of real-time precise point positioning programs to process GPS pseudorange observables, and validated their performance through static and kinematic positioning tests. To correct inaccurate broadcast orbits and clocks, and account for signal delays occurring from the ionosphere and troposphere, we applied State Space Representation (SSR) error corrections provided by the Seoul Broadcasting System (SBS) in South Korea. Site displacements due to solid earth tide loading are also considered for the purpose of improving the positioning accuracy, particularly in the height direction. When the developed algorithm was tested under static positioning, Kalman-filtered solutions produced a root-mean-square error (RMSE) of 0.32 and 0.40 m in the horizontal and vertical directions, respectively. For the moving platform, the RMSE was found to be 0.53 and 0.69 m in the horizontal and vertical directions.
Pub.: 10 Jun '17, Pinned: 28 Jun '17
Abstract: Satellite orbit and clock corrections are always treated as known quantities in GPS positioning models. Therefore, any error in the satellite orbit and clock products will probably cause significant consequences for GPS positioning, especially for real-time applications. Currently three types of satellite products have been made available for real-time positioning, including the broadcast ephemeris, the International GNSS Service (IGS) predicted ultra-rapid product, and the real-time product. In this study, these three predicted/real-time satellite orbit and clock products are first evaluated with respect to the post-mission IGS final product, which demonstrates cm to m level orbit accuracies and sub-ns to ns level clock accuracies. Impacts of real-time satellite orbit and clock products on GPS point and relative positioning are then investigated using the P3 and GAMIT software packages, respectively. Numerical results show that the real-time satellite clock corrections affect the point positioning more significantly than the orbit corrections. On the contrary, only the real-time orbit corrections impact the relative positioning. Compared with the positioning solution using the IGS final product with the nominal orbit accuracy of ~2.5 cm, the real-time broadcast ephemeris with ~2 m orbit accuracy provided <2 cm relative positioning error for baselines no longer than 216 km. As for the baselines ranging from 574 to 2982 km, the cm-dm level positioning error was identified for the relative positioning solution using the broadcast ephemeris. The real-time product could result in <5 mm relative positioning accuracy for baselines within 2982 km, slightly better than the predicted ultra-rapid product.
Pub.: 13 Jun '17, Pinned: 28 Jun '17
Abstract: Ionospheric phase scintillation can cause errors or outage in GNSS navigation solutions. Timely detection of phase scintillation will enable adaptive processing to mitigate its effects on navigation solutions. This paper presents a machine learning algorithm to autonomously detect phase scintillation based on frequency domain features. Validation using data from Gakona shows phase scintillation detection accuracy around 92 percent. Test results using data from Poker Flat, Jicamarca, Singapore, and Hong Kong demonstrate the capability of the trained detector to be applied more generally. Performance evaluation reveals that the values of phase scintillation index σϕ may be poor indications of scintillation activities. Concurrent phase and amplitude scintillation detection using similar machine learning algorithms is further investigated with low-latitude data. Results suggest that at low latitudes an amplitude detector alone is sufficient to capture scintillation in general, while at high latitudes, a phase scintillation detector is necessary to capture the dominating phase scintillation events. Copyright © 2017 Institute of Navigation
Pub.: 06 Jun '17, Pinned: 28 Jun '17
Abstract: Maximum likelihood estimation (MLE) has been researched for some acquisition and tracking applications of global navigation satellite system (GNSS) receivers and shows high performance. However, all current methods are derived and operated based on the sampling data, which results in a large computation burden. This paper proposes a low-complexity MLE carrier tracking loop for weak GNSS signals which processes the coherent integration results instead of the sampling data. First, the cost function of the MLE of signal parameters such as signal amplitude, carrier phase, and Doppler frequency are used to derive a MLE discriminator function. The optimal value of the cost function is searched by an efficient Levenberg-Marquardt (LM) method iteratively. Its performance including Cramér-Rao bound (CRB), dynamic characteristics and computation burden are analyzed by numerical techniques. Second, an adaptive Kalman filter is designed for the MLE discriminator to obtain smooth estimates of carrier phase and frequency. The performance of the proposed loop, in terms of sensitivity, accuracy and bit error rate, is compared with conventional methods by Monte Carlo (MC) simulations both in pedestrian-level and vehicle-level dynamic circumstances. Finally, an optimal loop which combines the proposed method and conventional method is designed to achieve the optimal performance both in weak and strong signal circumstances.
Pub.: 24 Jun '17, Pinned: 28 Jun '17
Abstract: Recent studies have shown that weak global navigation satellite system (GNSS) signals could potentially be used to navigate from Earth to the Moon. This would increase autonomy, robustness, and flexibility of the navigation architectures for future lunar missions. However, the utilization of GNSS signals at very high altitudes close to the Moon can be significantly limited by the very low power levels seen at the receiver's antenna. This can result in a strongly reduced visibility of the GNSS satellites, which can worsen the already poor relative geometry of the GNSS receiver to the GNSS satellites. Furthermore, during most of a Moon transfer orbit (MTO), the very weak GNSS signals are also affected by Doppler shifts and Doppler rates larger than the ones generally experienced on Earth, due to the much higher relative dynamics between the receiver and the transmitters. As a consequence, commercial GNSS receivers for terrestrial use cannot successfully acquire and track such signals. More advanced architectures and specific implementations are thus required to use GNSS for lunar missions. In this paper, we propose the use of an adaptive orbital filter to aid the GNSS acquisition and tracking modules and to strongly increase the achievable navigation accuracy. The paper describes the orbital filter architecture and tests results carried out by processing realistic radio frequency (RF) signals generated by our Spirent GSS 8000 full constellation simulator for a highly elliptical MTO. Copyright © 2017 Institute of Navigation
Pub.: 05 Jun '17, Pinned: 28 Jun '17