A novel high-order target phase approach (TPhA) for the station-keeping of periodic orbits is proposed in this work. The key elements of the TPhA method, the phase-angle Poincaré map and high-order maneuver map, are constructed using differential algebra (DA) techniques to determine station-keeping epochs and calculate correction maneuvers. A stochastic optimization framework tailored for the TPhA-based station-keeping process is leveraged to search for fuel-optimal and error-robust TPhA parameters. Quasi-satellite orbits (QSOs) around Phobos are investigated to demonstrate the efficacy of TPhA in mutli-fidelity dynamical models. Monte Carlo simulations demonstrated that the baseline QSO of JAXA’s Martian Moons eXploration (MMX) mission could be maintained with a monthly maneuver budget of approximately 1 m/s.

An algorithm for robust initial orbit determination (IOD) under perturbed orbital dynamics is presented. By leveraging map inversion techniques defined in the algebra of Taylor polynomials, this tool returns a highly accurate solution to the IOD problem and estimates a range centered on the aforementioned solution in which the true orbit should lie. To meet the specified accuracy requirements, automatic domain splitting is used to wrap the IOD routines and ensure that the local truncation error, introduced by a polynomial representation of the state estimate, remains below a predefined threshold. The algorithm is presented for three types of ground-based sensors, namely range radars, Doppler-only radars, and optical telescopes, by considering their different constraints in terms of available measurements and sensor noise. Finally, the improvement in performance with respect to a Keplerian-based IOD solution is demonstrated using large-scale numerical simulations over a subset of tracked objects in low Earth orbit.

Spacecraft conjunction management plays a crucial role in the mitigation of space collisions. When a conjunction event occurs, resources and time are spent analyzing, planning, and potentially maneuvering the spacecraft. This work contributes to a subpart of the problem: Confidently identifying events that will not lead to a high collision probability, and therefore do not require further investigation. The method reduces the dimensionality of the data via principal component analysis (PCA) on a subset of features. High-risk regions are then determined by clustering the projected data, and events that do not belong to a high-risk cluster are pruned. A genetic algorithm (GA) is developed to optimize the number of clusters and feature selection of the model. Furthermore, an ensemble learning framework is proposed to combine the suboptimal models for better generalization. The results show that the first set of parameters pruned approximately 50% of the events in the testing set with no false negatives, whereas the second set of parameters pruned 70% of the events and maintained a near-perfect recall. These results could benefit the optimization of operational resources and allow operators to focus better on the events of interest.

Space debris have become exceedingly dangerous over the years as the number of objects in orbit continues to increase. Active debris removal (ADR) missions have gained significant interest as effective means of mitigating the risk of collision between objects in space. This study focuses on developing a multi-ADR mission that utilizes controlled reentry and deorbiting. The mission comprises two spacecraft: a Servicer that brings debris to a low altitude and a Shepherd that rendezvous with the debris to later perform a controlled reentry. A preliminary mission design tool (PMDT) was developed to obtain time and fuel optimal trajectories for the proposed mission while considering the effect of J₂, drag, eclipses, and duty cycle. The PMDT can perform such trajectory optimizations for multi-debris missions with computational time under a minute. Three guidance schemes are also studied, taking the PMDT solution as a reference to validate the design methodology and provide guidance solutions to this complex mission profile.