Phase Errors Simulation Analysis for GNSS Antenna in Multipath Environment

High-precision GNSS application requires the exact phase center calibration of antenna. Various methods are published to determine the locations of the phase center. In the outfield, when the phase errors that arose by multipath exceed the phase center variations (PCV) tolerance, the calibration values may be not useful. The objective of this paper is thus to evaluate the phase errors that arose by multipath signals. An improved model of antenna receiving signal is presented. The model consists of three main components: (1) an antenna model created by combination of right hand circular polarization (RHCP) and left hand circular polarization (LHCP), (2) a multipath signals model including amplitude, phase, and polarization, and (3) a ground reflection model applying to circular polarization signals. Based on the model, two kinds of novel up-to-down (U/D) ratios are presented. The performance of the model is assessed against the impact of up-to-down ratio of antenna on phase errors

Incorporation of GNSS multipath to improve autonomous rendezvous, docking and proximity operations in space

Automated rendezvous and docking (AR&D;) operations are important for many future space missions, such as the resupply of space stations, repair and refueling of large satellites, and active removal of orbital debris. These operations depend critically on accurate, real-time knowledge of the relative position and velocity between two space vehicles. Unfortunately, Global Navigation Satellite System (GNSS) capabilities remain severely limited in close proximity to large space structures due to significant multipath effects and signal blockage. Although GNSS is used for the initial stages of approach, other instruments such as laser, radar and vision-based systems, are required to augment GNSS during AR&D; over the last few hundred meters. This dissertation evaluates the feasibility of GNSS multipath-based relative space navigation. Methods for separating and interpreting reflected signals are demonstrated using GNSS data collected during Hubble Servicing Mission 4 (HSM4), a model of the mission geometry, electromagnetic (EM) ray tracing, and a custom GNSS software receiver. EM ray tracing is used to show that a number of signals sufficient for ranging are reflected by the Hubble Space Telescope (HST) during HSM4, and the properties of these reflections are used to generate simulated GNSS data. The impact of reflected signals on code correlation shape, code tracking error, and pseudorange measurement is demonstrated using the simulated and experimental data. Relative navigation is demonstrated using simulated reflected signal measurements and the dependence of relative navigation on the reflecting object’s scattering properties is illustrated. From the tracking of data from two oppositely polarized antennas, both simulated and experimental, it is determined that multipath measurements are limited by system properties such as antenna polarization quality and front end bandwidth. Design considerations involved in optimizing a receiver to measure reflected signals are discussed

Expected Position Error for an Onboard Satellite GPS Receiver

The Global Positioning System (GPS) constellation provides ranging information that delivers inexpensive, high precision positioning for terrestrial users. Satellites in Low Earth Orbit (LEO) can use an onboard GPS receiver resulting in meter-level navigation solution accuracy. There are limitations to using GPS for positioning for satellites above LEO. The number of GPS satellites who\u27s signal can be received decreases as the receivers altitude approaches that of the GPS constellation. Above the GPS constellation, the available GPS signals for ranging will originate from satellites on the opposite side of Earth. This research calculates the available GPS signals to the receiver and determines the expected position error, while considering the effects from a low signal to noise ratio, poor geometry, and signal shift caused by high relative velocity

Advances in Measurement and Force Modeling for Improved GNSS-based Precise Orbit Determination of CYGNSS and Sentinel-6 MF

Precise orbit determination (POD) based on global navigation satellite systems (GNSS) tracking is fundamental to many space-based geodesy missions. The research presented here develops and implements improvements to the models and methods for two missions: CYGNSS, a lowcost constellation of small satellites, and Sentinel-6 Michael Freilich (MF), the current reference global ocean altimeter mission. The orbit solutions are improved though the advancement of the measurement models, dynamic force models, and solution strategies. CYGNSS is a constellation of eight small satellites designed to use reflected GNSS signals for retrieval of ocean surface winds. The navigation requirements to achieve this primary mission are quite loose, allowing the project to use simple point positioning, with a single-frequency GPS receiver, to support mission orbit needs. Research presented here demonstrates that orbits with 3-D positioning accuracy better than 10 cm can be achieved, with an iterative solution strategy that includes calibration of the antenna, use of combined code and carrier GRAPHIC (GRoup And PHase Ionosphere Correction) observables, and correction of a timing difference between code and carrier measurements. The process is validated using comparable data from the GRACE (Gravity Recovery and Climate Experiment) mission, for which high precision reference orbits are available. To support stringent POD requirements, Sentinel-6 MF is equipped with multiple tracking instruments: a TriG GPS receiver, a pair of redundant PODRIX GNSS (GPS + Galileo) receivers, a satellite laser retroreflector, and a Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) receiver. The first study develops an improved dynamic solar radiation pressure model. Compared to the previously used macromodel, this results in more consistent estimates of drag and solar scale parameters throughout changes in the orientation of the sun relative to the orbit plane (beta angle). The second study improves the measurement model by extending the new GPS IIIA transmitter antenna calibration out to boresight angles of 14-17 degrees, which are not observed by ground-based receivers, but are quite important for receivers in low Earth orbit. Implementation of this extension produces solutions that incorporate GPS IIIA measurements with statistics consistent with older satellite families. Finally, applying lessons learned from the previous studies, orbit solutions are generated from all available Sentinel-6 MF GNSS tracking data. This multi-receiver/GNSS configuration with two independent receivers and constellations (GPS + Galileo) revealed a range bias effect in the TriG GNSS observations that can be calibrated. Processing the calibrated TriG and PODRIX observations separately results in highly accurate orbit solutions, which are both consistent with one-way satellite laser ranging (SLR) residuals at the level of 6.9 mm rms. When processed together, the TriG plus PODRIX multi-GNSS solutions produced the most accurate orbit solutions with one-way SLR residual rms of 6.8 mm</p

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

Experimental evaluation of a UWB-based cooperative positioning system for pedestrians in GNSS-denied environment

Cooperative positioning (CP) utilises information sharing among multiple nodes to enable positioning in Global Navigation Satellite System (GNSS)-denied environments. This paper reports the performance of a CP system for pedestrians using Ultra-Wide Band (UWB) technology in GNSS-denied environments. This data set was collected as part of a benchmarking measurement campaign carried out at the Ohio State University in October 2017. Pedestrians were equipped with a variety of sensors, including two different UWB systems, on a specially designed helmet serving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go mode along trajectories with predefined checkpoints and under various challenging environments. In the developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurements are used for positioning of the pedestrians. It is realised that the proposed system can achieve decimetre-level accuracies (on average, around 20 cm) in the complete absence of GNSS signals, provided that the measurements from infrastructure nodes are available and the network geometry is good. In the absence of these good conditions, the results show that the average accuracy degrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperative range observations further enhances the positioning accuracy and, in extreme cases when only one infrastructure measurement is available, P2P CP may reduce positioning errors by up to 95%. The complete test setup, the methodology for development, and data collection are discussed in this paper. In the next version of this system, additional observations such as the Wi-Fi, camera, and other signals of opportunity will be included

Software-defined radio technology for GNSS scintillation analysis: bring Antarctica to the lab

Global navigation satellite systems (GNSSs) are widely used to support logistics, scientific operations, and to monitor the polar ionosphere indirectly, which is a region characterized by strong phase scintillation events that severely affect the quality and reliability of received signals. Professional commercial GNSS receivers are widely used for scintillation monitoring; on the contrary, custom-designed solutions based on data grabbers and software receivers constitute novelty. The latter enables a higher level of flexibility and configurability, which is important when working in remote and severe environments. We describe the scientific, technological, and logistical challenges of installing an ionospheric monitoring station in Antarctica, based on a multi-constellation and multi-frequency GNSS data grabber and a software-defined radio receiver. Having access to the full receiver chain and to intermediate signal processing stages allows a deep analysis of the impact of scintillation and, in turn, a better understanding of the physical phenomenon. The possibility to process high-resolution raw intermediate frequency samples of the signal enables not only the computation of scintillation indexes with the same quality as professional devices but also the design and test of innovative receiver architectures and algorithms. Furthermore, the record and replay approach offers the possibility to process in the lab the signals captured on site, with high fidelity level. It is like being in Antarctica again, but with an unlimited set of receivers and higher computational, storage, and bandwidth resources. The main advantages and disadvantages of this approach are analyzed. Examples of monitoring results are reported, confirming the monitoring capabilities, showing the good agreement with commercial receiver outputs and confirming the validity of post-processing and re-play operations

Experimental Evaluation of a UWB-Based Cooperative Positioning System for Pedestrians in GNSS-Denied Environment

Cooperative positioning (CP) utilises information sharing among multiple nodes to enable positioning in Global Navigation Satellite System (GNSS)-denied environments. This paper reports the performance of a CP system for pedestrians using Ultra-Wide Band (UWB) technology in GNSS-denied environments. This data set was collected as part of a benchmarking measurement campaign carried out at the Ohio State University in October 2017. Pedestrians were equipped with a variety of sensors, including two different UWB systems, on a specially designed helmet serving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go mode along trajectories with predefined checkpoints and under various challenging environments. In the developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurements are used for positioning of the pedestrians. It is realised that the proposed system can achieve decimetre-level accuracies (on average, around 20 cm) in the complete absence of GNSS signals, provided that the measurements from infrastructure nodes are available and the network geometry is good. In the absence of these good conditions, the results show that the average accuracy degrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperative range observations further enhances the positioning accuracy and, in extreme cases when only one infrastructure measurement is available, P2P CP may reduce positioning errors by up to 95%. The complete test setup, the methodology for development, and data collection are discussed in this paper. In the next version of this system, additional observations such as the Wi-Fi, camera, and other signals of opportunity will be included

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT