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PhD Student


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.


A Forward GPS Multipath Simulator Based on the Vegetation Radiative Transfer Equation Model.

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

Impacts of Satellite Orbit and Clock on Real-Time GPS Point and Relative Positioning.

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

A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals.

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

Orbital Filter Aiding of a High Sensitivity GPS Receiver for Lunar Missions

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