Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networks

Magnetic navigation (MagNav) is a rising alternative to the Global Positioning System (GPS) and has proven useful for aircraft navigation. Traditional aircraft navigation systems, while effective, face limitations in precision and reliability in certain environments and against attacks. Airborne MagNav leverages the Earth's magnetic field to provide accurate positional information. However, external magnetic fields induced by aircraft electronics and Earth's large-scale magnetic fields disrupt the weaker signal of interest. We introduce a physics-informed approach using Tolles-Lawson coefficients for compensation and Liquid Time-Constant Networks (LTCs) to remove complex, noisy signals derived from the aircraft's magnetic sources. Using real flight data with magnetometer measurements and aircraft measurements, we observe up to a 64% reduction in aeromagnetic compensation error (RMSE nT), outperforming conventional models. This significant improvement underscores the potential of a physics-informed, machine learning approach for extracting clean, reliable, and accurate magnetic signals for MagNav positional estimation.Comment: Accepted at the NeurIPS 2023 Machine Learning and the Physical Sciences workshop, 7 pages, 4 figures, see code here: https://github.com/fnerrise/LNNs_MagNav

Real-time Aerial Magnetic and Vision-aided Navigation

Aerial magnetic navigation has shown to be a viable alternative navigation method that has the potential for world-wide availability, to include over oceans. Obtaining GPS-level accuracy using magnetic navigation alone is challenging, but magnetic navigation can be combined with other alternative navigation methods that are more posed to obtaining GPS-level accuracy in their current state. This research presents an aerial navigation solution combining magnetic navigation and vision-aided navigation to aid an inertial navigation system (INS). The navigation solution was demonstrated in real-time playback using simulated magnetic field measurements and flight-test captured visual imagery. Additionally, the navigation solution was flight-tested on a USAF F-16 to demonstrate magnetic navigation in the challenging magnetic environment seen on operationally representative dynamic platforms

Artificial Intelligence-Assisted Inertial Geomagnetic Passive Navigation

In recent years, the integration of machine learning techniques into navigation systems has garnered significant interest due to their potential to improve estimation accuracy and system robustness. This doctoral dissertation investigates the use of Deep Learning combined with a Rao-Blackwellized Particle Filter for enhancing geomagnetic navigation in airborne simulated missions. A simulation framework is developed to facilitate the evaluation of the proposed navigation system. This framework includes a detailed aircraft model, a mathematical representation of the Earth\u27s magnetic field, and the incorporation of real-world magnetic field data obtained from online databases. The setup allows an accurate assessment of the performance and effectiveness of the proposed Geomagentic architecture in diverse and realistic geomagnetic scenarios. The results of this research demonstrate the potential of Machine Learning algorithms in improving the performance of the sensor fusion filter for geomagnetic navigation, and introduces a novel approach for resolution enhancing of available geomagnetic models, which provides a better description of the magnetic features within these models. The integration leads to more accurate and robust inertial guidance in airborne missions, thus paving the way for advanced, reliable navigation systems for a variety of aerial vehicles. Overall, this dissertation contributes to the state-of-the-art in geomagnetic navigation research by offering a novel approach to integrating machine learning techniques with traditional estimation methods, with a novel technique to obtain more accurate geomagnetic models required within these navigation architectures. The findings of this work hold promise for the development of advanced, adaptive navigation systems for both civilian and military aviation applications

Advanced Sensing, Fault Diagnostics, and Structural Health Management

Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries. With the advancement of science and technology, modern structural and mechanical systems are becoming more and more complex. Due to the continuous nature of operation and utilization, modern systems are heavily susceptible to faults. Hence, the operational reliability and safety of the systems can be greatly enhanced by using the multifaced strategy of designing novel sensing technologies and advanced intelligent algorithms and constructing modern data acquisition systems and structural health monitoring techniques. As a result, this research domain has been receiving a significant amount of attention from researchers in recent years. Furthermore, the research findings have been successfully applied in a wide range of fields such as aerospace, manufacturing, transportation and processes

Lithospheric structure from forward and inverse modeling of satellite gravity and magnetic data

Satellite missions have provided the Earth's gravity and magnetic field at resolutions sufficient for large-scale geophysical applications. While satellite data do not possess the same resolution as ground data, their homogeneous coverage and low error make them ideal for studying large-scale lithospheric structure and processes. In this thesis, the sensitivity of satellite potential field data to structures in the lithosphere is investigated. Furthermore, a crustal model is derived based on a data base of active seismic experiments. The calculation of the crustal model is data-driven, unlike previous crustal models, which typically rely on expert knowledge. Finally, a simple probabilistic joint inversion of gravity gradients and topography is developed, that is capable of estimating a two-layer density model of the lithosphere and its uncertainty.Die global verfügbaren Messungen des Schwere- und Magnetfeldes mithilfe von Satelliten liegen heute in einer Auflösung vor, die großräumige geophysikalische Anwendungen ermöglichen. Zwar ist es mit Satelliten nicht möglich, die Auflösung von Bodenmessungen zu erreichen. Um aber großkalige lithosphärische Strukturen zu untersuchen, sind Satellitendaten ideal, da die Datenqualität sehr homogen ist und sie einen geringen Messfehler aufweisen. In dieser Dissertation wird untersucht, wie groß die Sensitivität von Satellitendaten tatsächlich ist. Darüber hinaus wird ein globales Krustenmodell basierend auf einer Datenbank aktiver seismischer Profile erstellt, das -- anders als vorherige Krustenmodelle -- weitgehend ohne manuelle Eingabe von Expertenwissen auskommt. Zuletzt werden die Ergebnisse der verschiedenen Kapitel in einer gemeinsamen, probabilistischen Inversion von Schweregradienten und Topografie kombiniert, mit der ein einfaches Zwei-Schicht Dichtemodell der Lithosphäre und dessen Unsicherheit bestimmt werden kann

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

The national geomagnetic initiative

The Earth's magnetic field, through its variability over a spectrum of spatial and temporal scales, contains fundamental information on the solid Earth and geospace environment (the latter comprising the atmosphere, ionosphere, and magnetosphere). Integrated studies of the geomagnetic field have the potential to address a wide range of important processes in the deep mantle and core, asthenosphere, lithosphere, oceans, and the solar-terrestrial environment. These studies have direct applications to important societal problems, including resource assessment and exploration, natural hazard mitigation, safe navigation, and the maintenance and survivability of communications and power systems on the ground and in space. Studies of the Earth's magnetic field are supported by a variety of federal and state agencies as well as by private industry. Both basic and applied research is presently supported by several federal agencies, including the National Science Foundation (NSF), U.S. Geological Survey (USGS), U.S. Department of Energy (DOE), National Oceanic and Atmospheric Administration (NOAA), National Aeronautics and Space Administration (NASA), and U.S. Department of Defense (DOD) (through the Navy, Air Force, and Defense Mapping Agency). Although each agency has a unique, well-defined mission in geomagnetic studies, many areas of interest overlap. For example, NASA, the Navy, and USGS collaborate closely in the development of main field reference models. NASA, NSF, and the Air Force collaborate in space physics. These interagency linkages need to be strengthened. Over the past decade, new opportunities for fundamental advances in geomagnetic research have emerged as a result of three factors: well-posed, first-order scientific questions; increased interrelation of research activities dealing with geomagnetic phenomena; and recent developments in technology. These new opportunities can be exploited through a national geomagnetic initiative to define objectives and encourage coordination of efforts among federal and state agencies, academic institutions, and industry to systematically characterize the spatial and temporal behavior of the Earth's magnetic field on local, regional, and global scales in order to understand the physical processes in the solid earth and geospace environment, and to apply this understanding to a variety of scientific problems and to technical and societal needs

Lithosphere 2021 : Eleventh symposium on structure, composition and evolution of the lithosphere

Programme and extended abstract