ROSIA: Rotation-Search-Based Star Identification Algorithm

This paper presents a rotation-search-based approach for addressing the star identification (Star-ID) problem. The proposed algorithm, ROSIA, is a heuristics-free algorithm that seeks the optimal rotation that maximally aligns the input and catalog stars in their respective coordinates. ROSIA searches the rotation space systematically with the Branch-and-Bound (BnB) method. Crucially affecting the runtime feasibility of ROSIA is the upper bound function that prioritizes the search space. In this paper, we make a theoretical contribution by proposing a tight (provable) upper bound function that enables a 400x speed-up compared to an existing formulation. Coupling the bounding function with an efficient evaluation scheme that leverages stereographic projection and the R-tree data structure, ROSIA achieves feasible operational speed on embedded processors with state-of-the-art performances under different sources of noise. The source code of ROSIA is available at https://github.com/ckchng/ROSIA.Comment: 21 pages, 16 figures, Accepted to IEEE Transactions on Aerospace and Electronic System

Mathematical Optimisation for Vision-based Problems in Space Domain Awareness

By enabling essential services such as GPS navigation, weather forecasting, satellite communications, etc., space technologies are vital to our daily lives. However, with the growth of space-enabled services and space utilisation in general, there is a risk of congestion in space. Space Domain Awareness (SDA) is the discipline of monitoring, understanding, and predicting events in the space environment. With the ever-increasing number of space assets that are being launched, gaining SDA has become important towards maintaining the safe and sustainable usage of space. Within the broad range of SDA tasks, this thesis focuses on three problems that operate on optical data: star identification (Star-ID), initial orbit determination (IOD), and light-curve inversion. The targeted problems have mainly been addressed in the literature with heuristic methods. In contrast, this thesis advocates the use of mathematical optimisation, which seeks the best solution in a defined search space guided by a clear-cut objective function. In the context of SDA, mathematical optimisation offers three advantages: it minimises the need for hyperparameter tuning, mitigates the risk of error propagation, and allows solution optimality analysis. The Star-ID task aims to identify the imaged stars. Existing approaches employ the established feature-extraction-then-identification pipeline, leading to increased hyperparameters and error-prone heuristics. This thesis introduces a Star-ID method that searches directly in the rotation space, thereby obviating the feature extraction process. The proposed method solves Star-ID as a maximisation problem with the Branch-and-Bound (BnB) method, where the solution’s optimality is guaranteed. IOD is the process of determining the orbital parameters of a space object. Existing IOD methods assume given accurate resolved optical data, i.e., the line-of-sight (LOS) vectors, which are challenging to obtain. Furthermore, any error made in the LOS estimation process is propagated to the IOD solver, which can lead to inaccurate orbital solutions. This thesis introduces an IOD method that operates directly on raw optical data—the streak images, thereby preventing error propagation. The proposed method seeks the orbital parameters that map to the generated streak images that best fit the observed ones using a gradient descent approach. Finally, this thesis tackles the light-curve inversion problem, which seeks an object’s spin pole, spin period, shape, and scattering properties. The commonly adopted light-curve-inversion formulation is non-convex, where the globally optimum solution is hard to achieve with locally optimum methods. Existing methods exploit fast heuristics to increase the likelihood of obtaining a good solution. This thesis presents a novel light-curve-inversion formulation where the best spin pole and shape solutions are guaranteed to be achieved via the BnB method.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202

Microenvironmental Hypoxia Induces Dynamic Changes in Lung Cancer Synthesis and Secretion of Extracellular Vesicles

10.3390/cancers12102917CANCERS121