Context-aware Peer-to-Peer and Cooperative Positioning

Peer-to-peer and cooperative positioning represent one of the major evolutions for mass-market positioning, bringing together capabilities of Satellite Navigation and Communication Systems. It is well known that smartphones already provide user position leveraging both GNSS and information collected through the communication network (e.g., Assisted-GNSS). However, exploiting the exchange of information among close users can attain further benefits. In this paper, we deal with such an approach and show that sharing information on the environmental conditions that characterize the reception of satellite signals can be effectively exploited to improve the accuracy and availability of user positioning. This approach extends the positioning service to indoor environments and, in general, to any scenario where full visibility of the satellite constellation cannot be grante

Precise Orbit Determination of CubeSats

CubeSats are faced with some limitations, mainly due to the limited onboard power and the quality of the onboard sensors. These limitations significantly reduce CubeSats' applicability in space missions requiring high orbital accuracy. This thesis first investigates the limitations in the precise orbit determination of CubeSats and next develops algorithms and remedies to reach high orbital and clock accuracies. The outputs would help in increasing CubeSats' applicability in future space missions

GNSS-Based Navigation for Lunar Missions

Numerous applications, not only Earth-based, but also space-based, have strengthened the interest of the international scientific community in using Global Navigation Satellite Systems (GNSSs) as navigation systems for space missions that require good accuracy and low operating costs. Indeed, already used in Low Earth Orbits (LEOs), GNSS based-navigation GNSS-based navigation systems can maximize the autonomy of a spacecraft while reducing the costs of ground operations, allowing for budget-limited missions of micro- and nanosatellites. This is why GNSS is also very attractive for applications in higher Earth orbits up to the Moon, such as in Moon Transfer Orbits (MTOs). However, while GNSS receivers have already been exploited with success for LEOs, their use in higher Earth orbits above the GNSS constellation is extremely challenging, particularly on the way from the Earth to the Moon, characterized by weaker signals with wider gain variability, larger dynamic ranges resulting in higher Doppler and Doppler rates, critically lower satellite signal availability, and poorer satellite-to-user geometry. In this context, the first research objective and achievement of this PhD research is a feasibility study of GNSS as an autonomous navigation system to reach the Moon, and the determination of the requirements for the design of a code-based GNSS receiver for such a mission. The most efficient combinations of signals transmitted by the GPS, Galileo, and combined GPS-Galileo constellations have been identified by analyzing the theoretical achievable signal acquisition and tracking sensitivities, the resultant constellation availability, the pseudorange error factors, and the geometry error factor and accordingly the achievable navigation performance The results show that GNSS signals can be tracked up to Moon altitude, but not with the current GNSS receiver technology for terrestrial use. The second research objective and achievement is the design and implementation of a GNSS receiver proof-of-concept capable of providing GNSS observations onboard a space vehicle orbiting up to Moon altitude. This research work describes the hardware architecture, the high-sensitivity acquisition and tracking modules and the standalone single-epoch navigation performance of the developed GPS L1 C/A hard-ware receiver, named the WeakHEO receiver. Although they can still be collected, GNSS observations at Moon altitude, if not filtered, but simply used to compute a single-epoch least-squares solution, lead to a very coarse navigation accuracy, on the order of 1 to 10 km, depending on the number and type of signals successfully processed. Therefore, the third and main research objective and achievement is the design and implementation of a GNSS-based orbital filter (OF) determination unit, based on an extended Kalman filter (EKF) and an orbital forces model, able to significantly improve the achievable navigation performance and also to aid acquisition and tracking modules of the GNSS receiver. Simulation results of the OF performance when processing simulated GPS and Galileo observations, but also real GPS L1 C/A observations provided by the WeakHEO receiver (when connected in a hardware in the loop configuration to a full constellation GNSS radio frequency signal simulator), show a positioning accuracy at Moon altitude of a few hundred meters

A New Cooperative PPP-RTK System with Enhanced Reliability in Challenging Environments

Compared to the traditional PPP-RTK methods, cooperative PPP-RTK methods provide expandable service coverage and eliminate the need for a conventional expensive data processing center and the establishment and maintenance of a permanently deployed network of dense GNSS reference stations. However, current cooperative PPP-RTK methods suffer from some major limitations. First, they require a long initialization period before the augmentation service can be made available from the reference stations, which decreases their usability in practical applications. Second, the inter-reference station baseline ambiguity resolution (AR) and regional atmospheric model, as presented in current state-of-art PPP-RTK and network RTK (NRTK) methods, are not utilized to improve the accuracy and service coverage of the network augmentation. Third, the positioning performance of current PPP-RTK methods would be significantly degraded in challenging environments due to multipath effects, non-line-of-sight (NLOS) errors, poor satellite visibility and geometry caused by severe signal blockages. Finally, current position domain or ambiguity domain partial ambiguity resolution (PAR) methods suffer from high false alarm and miss detection, particularly in challenging environments with poor satellite geometry and observations contaminated by NLOS effect, gross errors, biases, and high observation noise. This thesis proposed a new cooperative PPP-RTK positioning system, which offers significant improvements to provide fast-initialization, scalable coverage, and decentralized real-time kinematic precise positioning with enhanced reliability in challenging environments. The system is composed of three major components. The first component is a new cooperative PPP-RTK framework in which a scalable chain of cooperative static or moving reference stations, generates single reference station-derived or reference station network-derived state-space-representation (SSR) corrections for fast ambiguity resolution at surrounding user stations with no need for a conventional expensive data processing center. The second component is a new multi-feature support vector machine (SVM) signal classifier based weight scheme for GNSS measurements to improve the kinematic GNSS positioning accuracy in urban environments. The weight scheme is based on the identification of important features in GNSS data in urban environments and intelligent classification of line-of-sight (LOS) and NLOS signals. The third component is a new PAR method based on machine learning, which employs the combination of two support vector machine (SVM) to effectively identify and exclude bias sources from PAR without relying on satellite geometry. The prototype of the new PPP-RTK system is developed and substantially tested using publically available real-time SSR products from International GNSS Service (IGS) Real-Time Service (RTS)

Hybrid and Cooperative Positioning Solutions for Wireless Networks

In this thesis, some hybrid and cooperative solutions are proposed and analyzed to locate the user in challenged scenarios, with the aim to overcome the limits of positioning systems based on single technology. The proposed approaches add hybrid and cooperative features to some conventional position estimation techniques like Kalman filter and particle filter, and fuse information from different radio frequency technologies. The concept of cooperative positioning is enhanced with hybrid technologies, in order to further increase the positioning accuracy and availability. In particular, wireless sensor networks and radio frequency identification technology are used together to enhance the collected data with position information. Terrestrial ranging techniques (i.e., ultra-wide band technology) are employed to assist the satellite-based localization in urban canyons and indoors. Moreover, some advanced positioning algorithms, such as energy efficient, cognitive tracking and non-line-of-sight identification, are studied to satisfy the different positioning requirements in harsh indoor environments. The proposed hybrid and cooperative solutions are tested and verified by first Monte Carlo simulations then real experiments. The obtained results demonstrate that the proposed solutions can increase the robustness (positioning accuracy and availability) of the current localization system

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

Survey on Signal Processing for GNSS under Ionospheric Scintillation: Detection, Monitoring, and Mitigation

Ionospheric scintillation is the physical phenomena affecting radio waves coming from the space through the ionosphere. Such disturbance is caused by ionospheric electron density irregularities and is a major threat in Global Navigation Satellite Systems (GNSS). From a signal processing perspective, scintillation is one of the most challenging propagation scenarios, particularly affecting high-precision GNSS receivers and safety critical applications where accuracy, availability, continuity and integrity are mandatory. Under scintillation, GNSS signals are affected by amplitude and phase variations, which mainly compromise the synchronization stage of the receiver. To counteract these effects, one must resort to advanced signal processing techniques such as adaptive/robust methods, machine learning or parameter estimation. This contribution reviews the signal processing landscape in GNSS receivers, with emphasis on different detection, monitoring and mitigation problems. New results using real data are provided to support the discussion. To conclude, future perspectives of interest to the GNSS community are discussed

Entwicklung und Implementierung eines Peer-to-Peer Kalman Filters für Fußgänger- und Indoor-Navigation

Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to-Peer Kalman Filter (P2PKF)