Hang Woon Lee

Matthew C. Fox, Gavin M. Baker, David O. Williams Rogers et al.

The escalating accumulation of orbital debris poses a critical threat to future space operations. Space-based lasers leveraging laser ablation have emerged as a promising approach for mitigating debris proliferation and preserving the orbital environment. Current literature, however, treats space-based laser debris remediation as a deterministic problem, assuming that momentum transfer and the resulting debris perturbations are precisely known. In reality, laser-to-debris engagement outcomes are inherently stochastic due to partially known debris characteristics. Compounding this challenge, estimating critical laser-matter parameters in situ, such as the momentum coupling coefficient, requires ablation that consequently perturbs the debris trajectory. This establishes a coupled ablation-and-estimation problem in which the laser platform and target debris encounter geometry influences remediation effectiveness and estimation accuracy. To address this problem, we present a joint ablation-and-estimation methodology that provides insights into the driving factors that make different encounter geometries improve or degrade overall remediation and estimation performance. Results across multiple coplanar and out-of-plane encounter geometries demonstrate how periapsis-lowering capacity, linear system observability, and nonlinear estimation performance evolve as laser parameters and relative orbit geometry vary. By identifying the key drivers behind these metrics, this study highlights critical considerations for the safe and effective operation of space-based lasers under uncertainty.

Brycen D. Pearl, Joshua G. Warner, Hang Woon Lee

Wildfires are becoming increasingly frequent, with potentially devastating consequences, including loss of life, infrastructure destruction, and severe environmental damage. Low Earth orbit satellites equipped with onboard sensors can capture critical information relative to active wildfires and enable near real-time detection through machine learning algorithms applied to the acquired data. We propose a framework that automates the complete wildfire detection and satellite scheduling pipeline, entitled the WildFire-applicable Intelligent and Responsive Ensemble for Detection and Scheduling (WildFIRE-DS). This paper develops an algorithm to realize the vision of the WildFIRE-DS as a proof of concept, integrating three key components: wildfire detection in satellite imagery, statistical updating that incorporates data from repeated flyovers, and multi-satellite scheduling optimization. The algorithm enables wildfire detection using convolutional neural networks with sensor fusion techniques, incorporates subsequent flyover information via Bayesian statistics, and schedules a constellation of satellites using the state-of-the-art Reconfigurable Earth Observation Satellite Scheduling Problem. Simulated experiments conducted using real-world wildfire locations and the orbits of operational Earth observation satellites to demonstrate that this autonomous detection and scheduling approach effectively enhances wildfire monitoring capabilities.

David O. Williams Rogers, Matthew C. Fox, Paul R. Stysley et al.

The significant expansion of the orbital debris population poses a serious threat to the safety and sustainability of space operations. This paper investigates orbital debris remediation through a constellation of collaborative space-based lasers, leveraging the principle of momentum transfer onto debris via laser ablation. A novel delta-v vector analysis framework quantifies the cumulative effects of multiple concurrent laser-to-debris (L2D) engagements by utilizing the vector composition of the imparted delta-v vectors. The paper formulates the Concurrent Location-Scheduling Optimization Problem (CLSP) to optimize the placement of laser platforms and the scheduling of L2D engagements, aiming to maximize debris remediation capacity. Given the computational intractability of the CLSP, a decomposition strategy is employed, yielding two sequential subproblems: (1) determining optimal laser platform locations via the Maximal Covering Location Problem, and (2) scheduling L2D engagements using a novel integer linear programming approach to maximize debris remediation capacity. Computational experiments evaluate the efficacy of the proposed framework across diverse mission scenarios, demonstrating critical constellation functions such as collaborative and controlled nudging, deorbiting, and just-in-time collision avoidance. A sensitivity analysis further explores the impact of varying the number and distribution of laser platforms on debris remediation capacity, offering insights into optimizing the performance of space-based laser constellations.

Brycen D. Pearl, Logan P. Gold, Hang Woon Lee

Tropical cyclones (TCs) are highly dynamic natural disasters that travel vast distances and occupy a large spatial scale, leading to loss of life, economic strife, and destruction of infrastructure. The severe impact of TCs makes them crucial to monitor such that the collected data contributes to forecasting their trajectory and severity, as well as the provision of information to relief agencies. Among the various methods used to monitor TCs, Earth observation satellites are the most flexible, allowing for frequent observations with a wide variety of instruments. Traditionally, satellite scheduling algorithms assume nadir-directional observations, a limitation that can be alleviated by incorporating satellite agility and constellation reconfigurability -- two state-of-the-art concepts of operations (CONOPS) that extend the amount of time TCs can be observed from orbit. This paper conducts a systematic comparative analysis between both CONOPS to present the performance of each relative to baseline nadir-directional observations in monitoring TCs. A dataset of 100 historical TCs is used to provide a benchmark concerning real-world data through maximizing the number of quality observations. The results of the comparative analysis indicate that constellation reconfigurability allowing plane-change maneuvers outperforms satellite agility in the majority of TCs analyzed.

Brycen D. Pearl, Joseph M. Miller, Hang Woon Lee

Earth observation satellites (EOSs) play a pivotal role in capturing and analyzing planetary phenomena, ranging from natural disasters to societal development. The EOS scheduling problem (EOSSP), which optimizes the schedule of EOSs, is often solved with respect to nadir-directional EOS systems, thus restricting the observation time of targets and, consequently, the effectiveness of each EOS. This paper leverages state-of-the-art constellation reconfigurability to develop the reconfigurable EOS scheduling problem (REOSSP), wherein EOSs are assumed to be maneuverable, forming a more optimal constellation configuration at multiple opportunities during a schedule. This paper develops a novel mixed-integer linear programming formulation for the REOSSP to optimally solve the scheduling problem for given parameters. Additionally, since the REOSSP can be computationally expensive for large-scale problems, a rolling horizon procedure (RHP) solution method is developed. The performance of the REOSSP is benchmarked against the EOSSP, which serves as a baseline, through a set of random instances where problem characteristics are varied and a case study in which Hurricane Sandy is used to demonstrate realistic performance. These experiments demonstrate the value of constellation reconfigurability in its application to the EOSSP, yielding solutions that improve performance, while the RHP enhances computational runtime for large-scale REOSSP instances.

David O. Williams Rogers, Dongshik Won, Dongwook Koh et al.

Designing satellite constellation systems involves complex multidisciplinary optimization in which coverage serves as a primary driver of overall system cost and performance. Among the various design considerations, constellation configuration, which dictates how satellites are placed and distributed in space relative to each other, predominantly determines the resulting coverage. In constellation configuration design, coverage may be treated either as an optimization objective or as a constraint, depending on mission goals. State-of-the-art literature addresses each mission scenario on a case-by-case basis, employing distinct assumptions, modeling techniques, and solution methods. While such problem-specific approaches yield valuable insights, users often face implementation challenges when performing trade-off studies across different mission scenarios, as each scenario must be handled distinctly. In this paper, we propose a collection of five mixed-integer linear programs that are of practical significance, extensible to more complex mission narratives through additional constraints, and capable of obtaining provably optimal constellation configurations. The framework can handle various metrics and mission scenarios, such as percent coverage, average or maximum revisit times, a fixed number of satellites, spatiotemporally varying coverage requirements, and static or dynamic targets. The paper presents several case studies and comparative analyses to demonstrate the versatility of the proposed framework.