Doctor of Philosophy

dissertationThe need for position and orientation information in a wide variety of applications has led to the development of equally varied methods for providing it. Amongst the alternatives, inertial navigation is a solution that o ffers self-contained operation and provides angular rate, orientation, acceleration, velocity, and position information. Until recently, the size, cost, and weight of inertial sensors has limited their use to vehicles with relatively large payload capacities and instrumentation budgets. However, the development of microelectromechanical system (MEMS) inertial sensors now o ers the possibility of using inertial measurement in smaller, even human-scale, applications. Though much progress has been made toward this goal, there are still many obstacles. While operating independently from any outside reference, inertial measurement su ers from unbounded errors that grow at rates up to cubic in time. Since the reduced size and cost of these new miniaturized sensors comes at the expense of accuracy and stability, the problem of error accumulation becomes more acute. Nevertheless, researchers have demonstrated that useful results can be obtained in real-world applications. The research presented herein provides several contributions to the development of human-scale inertial navigation. A calibration technique allowing complex sensor models to be identified using inexpensive hardware and linear solution techniques has been developed. This is shown to provide significant improvements in the accuracy of the calibrated outputs from MEMS inertial sensors. Error correction algorithms based on easily identifiable characteristics of the sensor outputs have also been developed. These are demonstrated in both one- and three-dimensional navigation. The results show significant improvements in the levels of accuracy that can be obtained using these inexpensive sensors. The algorithms also eliminate empirical, application-specific simplifications and heuristics, upon which many existing techniques have depended, and make inertial navigation a more viable solution for tracking the motion around us

Signal Processing in Cold Atom Interferometry-Based INS

High precision Cold Atom Interferometers (CAI) are in development to supplement or replace conventional, navigation quality inertial measurement units. A major drawback of the atomic interferometers is their low duty cycle and sampling rate, caused by delays required for cooling the atoms and collecting acceleration and angular rate measurements. A method is herein developed for inertial navigation by integrating highly accurate, low duty cycle CAI measurements with high bandwidth, conventional Inertial Navigation System (INS) measurements. A xed-lag smoothing algorithm is used to estimate optimal acceleration and angular rate measurements from the CAI and INS data. Given current CAI limitations, simulation results demonstrate nearly 50 percent error reduction for the enhanced INS compared to a conventional, unaided INS. When the conventional INS position error was increased by 500 (m/hr), the 50 percent error reduction from aiding was maintained. Increasing the conventional INS data rate fifteen-fold while maintaining a 1 Hz CAI sample rate leads to an approximately 6 percent increase in navigation error, suggesting that the CAI- aiding algorithm effectivity is only slightly influenced by the conventional INS data rates. A five-fold increase of the CAI measurement rate shows approximately 80 percent reduction in navigation error, supporting the potential for significant performance gains in the near future from advancements in cold atom technology

Track detection in railway sidings based on MEMS gyroscope sensors

The paper presents a two-step technique for real-time track detection in single-track railway sidings using low-cost MEMS gyroscopes. The objective is to reliably know the path the train has taken in a switch, diverted or main road, immediately after the train head leaves the switch. The signal delivered by the gyroscope is first processed by an adaptive low-pass filter that rejects noise and converts the temporal turn rate data in degree/second units into spatial turn rate data in degree/meter. The conversion is based on the travelled distance taken from odometer data. The filter is implemented to achieve a speed-dependent cut-off frequency to maximize the signal-to-noise ratio. Although direct comparison of the filtered turn rate signal with a predetermined threshold is possible, the paper shows that better detection performance can be achieved by processing the turn rate signal with a filter matched to the rail switch curvature parameters. Implementation aspects of the track detector have been optimized for real-time operation. The detector has been tested with both simulated data and real data acquired in railway campaigns.Peer ReviewedPostprint (published version

Innovative Solutions for Navigation and Mission Management of Unmanned Aircraft Systems

The last decades have witnessed a significant increase in Unmanned Aircraft Systems (UAS) of all shapes and sizes. UAS are finding many new applications in supporting several human activities, offering solutions to many dirty, dull, and dangerous missions, carried out by military and civilian users. However, limited access to the airspace is the principal barrier to the realization of the full potential that can be derived from UAS capabilities. The aim of this thesis is to support the safe integration of UAS operations, taking into account both the user's requirements and flight regulations. The main technical and operational issues, considered among the principal inhibitors to the integration and wide-spread acceptance of UAS, are identified and two solutions for safe UAS operations are proposed: A. Improving navigation performance of UAS by exploiting low-cost sensors. To enhance the performance of the low-cost and light-weight integrated navigation system based on Global Navigation Satellite System (GNSS) and Micro Electro-Mechanical Systems (MEMS) inertial sensors, an efficient calibration method for MEMS inertial sensors is required. Two solutions are proposed: 1) The innovative Thermal Compensated Zero Velocity Update (TCZUPT) filter, which embeds the compensation of thermal effect on bias in the filter itself and uses Back-Propagation Neural Networks to build the calibration function. Experimental results show that the TCZUPT filter is faster than the traditional ZUPT filter in mapping significant bias variations and presents better performance in the overall testing period. Moreover, no calibration pre-processing stage is required to keep measurement drift under control, improving the accuracy, reliability, and maintainability of the processing software; 2) A redundant configuration of consumer grade inertial sensors to obtain a self-calibration of typical inertial sensors biases. The result is a significant reduction of uncertainty in attitude determination. In conclusion, both methods improve dead-reckoning performance for handling intermittent GNSS coverage. B. Proposing novel solutions for mission management to support the Unmanned Traffic Management (UTM) system in monitoring and coordinating the operations of a large number of UAS. Two solutions are proposed: 1) A trajectory prediction tool for small UAS, based on Learning Vector Quantization (LVQ) Neural Networks. By exploiting flight data collected when the UAS executes a pre-assigned flight path, the tool is able to predict the time taken to fly generic trajectory elements. Moreover, being self-adaptive in constructing a mathematical model, LVQ Neural Networks allow creating different models for the different UAS types in several environmental conditions; 2) A software tool aimed at supporting standardized procedures for decision-making process to identify UAS/payload configurations suitable for any type of mission that can be authorized standing flight regulations. The proposed methods improve the management and safe operation of large-scale UAS missions, speeding up the flight authorization process by the UTM system and supporting the increasing level of autonomy in UAS operations

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

The 1st International Electronic Conference on Algorithms

This book presents 22 of the accepted presentations at the 1st International Electronic Conference on Algorithms which was held completely online from September 27 to October 10, 2021. It contains 16 proceeding papers as well as 6 extended abstracts. The works presented in the book cover a wide range of fields dealing with the development of algorithms. Many of contributions are related to machine learning, in particular deep learning. Another main focus among the contributions is on problems dealing with graphs and networks, e.g., in connection with evacuation planning problems

Estimating the orientation of a game controller from inertial and magnetic measurements

L’estimation de l’orientation d’un corps rigide en mouvement dans l’espace joue un rôle indispensable dans les technologies de navigation, par exemple, les systèmes militaires de missiles, les avions civils, les systèmes de navigation chirurgicale, la cartographie faite par des robots, les véhicules autonomes et les contrôleurs de jeux. Cette technique est maintenant utilisée dans certaines applications qui nous touchent directement, notamment dans les contrôleurs de jeux tels que la Wii-mote. Dans cette veine, la recherche présentée ici porte sur l’estimation de l’orientation d’un corps rigide à partir des mesures de capteurs inertiels et magnétiques peu coûteux. Comme les capteurs inertiels permettent de mesurer les dérivées temporelles de l’orientation, il est naturel de commencer par l’estimation de la vitesse angulaire. Par conséquent, nous présentons d’abord une nouvelle façon de déterminer la vitesse angulaire d’un corps rigide à partir d’accéléromètres. Ensuite, afin d’estimer l’orientation, nous proposons une nouvelle méthode d’estimation de l’orientation d’un corps rigide dans le plan vertical à partir des mesures d’accéléromètres, en discernant ses composantes inertielle et gravitationnelle. Mais, ce n’est sûrement pas suffisant d’estimer l’orientation dans le plan vertical, parce que la plupart des applications se produisent dans l’espace tridimensionnel. Pour estimer les rotations dans l’espace, nous présentons d’abord la conception d’un contrôleur de jeu, dans lequel tous les capteurs nécessaires sont installés. Ensuite, ces capteurs sont étalonnés pour déterminer leurs facteurs d’échelle et leurs zéros, de manière à améliorer leurs exactitudes. Ensuite, nous développons une nouvelle méthode d’estimation de l’orientation d’un corps rigide se déplaçant dans l’espace, encore en discernant les composantes gravitationnelle et inertielle des accélérations. Finalement, pour imiter le contrôleur de jeu Wii, nous créons une interface usager simple de sorte qu’une représentation virtuelle du contrôleur de jeu puisse suivre chaque mouvement du contrôleur de jeu conçu (réalité virtuelle). L’interface usager conçue montre que l’algorithme proposé est suffisamment précis pour donner à l’usager un contrôle fidèle de l’orientation du contrôleur de jeu virtuel.Estimating the orientation of a rigid-body moving in space is an indispensable component of navigation technology, e.g., military missile systems, civil aircrafts, surgical navigation systems, robot mapping, autonomous vehicles and game controllers. It has now come directly into some aspects of our lives, notoriously in game controllers, such as the Wiimote. In this vein, this research focuses on the development of new algorithms to estimate the rigid-body orientation from common inexpensive inertial and magnetic sensors. As inertial sensors measure the time derivatives of the orientation, it is natural to start with the estimation of the angular velocity. More precisely, we present a novel way of determining the angular velocity of a rigid body from accelerometer measurements. This method finds application in crashworthiness and motion analysis in sports, for example, where impacts forbid the use of mechanical gyroscopes. Secondly, in an attempt to estimate the orientation in a simplified setting, we propose a novel method of estimating the orientation of a rigid body in the vertical plane from point-acceleration measurements, by discerning its gravitational and inertial components. Thirdly, it is surely not enough to estimate the orientation in the vertical plane, because most applications take place in three dimensions. For estimating rotations in space, we first present the game controller design, in which all necessary sensors are installed. Then, these sensors are calibrated to determine their scale factors and offsets so as to improve their performances. Thence, we develop a novel method of estimating the orientation of a rigid body moving in space from inertial sensors, also by discerning the gravitational and inertial components of the acceleration. Finally, in order to imitate the game controller Wii, we create a simple user interface in which a virtual representative of the game controller follows every orientation of the true game controller (virtual reality). The user interface shows that the proposed algorithm is sufficiently accurate to give the user a transparent control of the orientation of the virtual game controller

Road Condition Estimation with Data Mining Methods using Vehicle Based Sensors

The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation