Research

Lunar Occultations (LO) are important techniques used to study extra-terrestrial and extra-solar phenomena. This investigation aims to design a spacecraft trajectory framework to study LO from the Earth-Moon stable Libration points (L4/L5). The relative position and stability properties of this location allows for increase observation time relative to LEO and ground observatories. The investigation also includes a Solar sail as a method of propulsion to be used for the orbit insertion maneuvers from Earth orbit. 

The Theory of Functional Connections (TFC) is used to solve Lambert’s problem. The mathematical model involves a functional approximation of the solution using orthogonal polynomials and a non-linear least squares solution. The solver has the ability to include any perturbation, namely J2 perturbation, third-body perturbations, and Solar radiation pressure. The algorithm performs faster than other solvers, namely differential corrections, and is generally more robust with the exception of a singularity when the transfer arc is close to 180 degrees.

The research develops an algorithm that lowers the computational time required to propagate a trajectory by solving for an analytical polynomial between boundary conditions. The algorithm is tested on many popular orbital trajectories in the Earth-Moon circular restricted three-body problem and compared to iterative methods. 

The research identifies key regions of interest within the Cislunar region, both on the Lunar surface and Cislunar space. Then, an orbital framework of low lunar orbits, that can enable passive information gain techniques, is developed to service the identified regions of interest on the Lunar surface.

The ability to predict the attitude of a spacecraft in complex dynamical environments such as the Cislunar region is necessary for the success of future missions. This research is an expansion of existing work done to develop the Circular Restricted Three-Body Problem, a reduction of the full N-body problem. The CRF3BP seeks to consider a rigid-body spacecraft in a 3-body system, allowing for attitude to be predicted in areas of space such as the Cislunar region and allowing for orbit/attitude coupling to be considered. The orbit that is analyzed was first considered in the CRF3BP as an initial guess, then transitioned into a full ephemeris model considering the Sun as well as the Moon's alliptical orbit to determine if the newly formulated CRF3BP was capable of accurately predicting attitude.

Identifying Resident Space Objects (RSOs) in arbitrary space imagery with little prior information is a challenging, yet crucial next step in space domain awareness applications. This work proposes improvements to an existing RSO identification process for unresolved space images. The algorithm has three main phases: image processing, star elimination, and RSO association. Star elimination and RSO association use nearest neighbor association and tresholds on inertial frame-to-frame motion of observations to associate objects. Given a set of unresolved space images contiguous in time, the product of the algorithm presented is a set of measurements for orbit estimation.

This investigation relates to creating a simulator (JAVA) that propagates the motion of satellites belonging to constellations, specifically with a telecommunications role. This simulator has the capability of accurately computing visibility to target areas, ground stations, and user equipment taking into account field-of-view, as well as computing charge generated during Sun-visibility events. An unscented Kalman filter and inter-satellite links are included in the algorithm, with link budget considerations. 

The aim of this research project is to perform essential investigations into the identification of UAVs (Unmanned Aerial Vehicles) using cutting-edge pattern recognition methods and quantum signal processing applied to recorded acoustic data. The primary objective is to recognize and predict deviations in audio signals by exploiting mathematical associations in the frequency domain, enabling the detection of UAVs in the surrounding area. The integration of the quantum signal processing framework improves current audio signal processing algorithms by harnessing the mathematical concepts of quantum mechanics. The foundational investigation undertaken in the envisioned project will lead to a groundbreaking progress in modern surveillance methods, with a particular emphasis on elevating the capabilities of identifying unidentifiable objects, refining positioning techniques, and advancing traffic management technology. By combining state-of-the-art research in induced vibration analysis with medical diagnosis, the project enables the development of effective and flexible patient diagnosis systems within medical drones, achieved through audio signal processing and machine learning techniques. This endeavour also serves as a unique educational resource, providing hands-on insights at the crossroads of pioneering algorithms and UAV technology, catering to both students and researchers. 

The aim of this research project is to use Augmented Reality (AR) in order to create an immersive experience for both design purposes in interplanetary missions and surveillance of the crowded Cislunar region. The framework will allow users to create, visualize, and interact with different trajectories in 3D space, with the objective of providing a better understanding of multidimensional astrodynamics data. Moreover, the surveillance capabilities will allow the facilitation of the mission design process by providing real-time locations of all objects in the Cislunar region, thus contributing to collision avoidance and the possibility of optimizing trajectory parameters for different missions.