arXiv subjects

Hang Woon Lee

Publications and source records attributed to Hang Woon Lee.

16 recordsLinked to original sources

arXiv2026

Encounter Geometry Effects on Space-Based Laser Debris Remediation and Estimation

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 influence 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.

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arXiv2026

Stochastic Multistage Constellation Reconfiguration Problem: Stochastic Dual Dynamic Integer Programming Approach

Observation tasking is critical to satellite operations, enabling observation of planetary phenomena, orbital debris monitoring, and space domain awareness. To improve the effectiveness of satellite operations, orbital maneuverability is introduced as a leading-edge concept of operations within constellation reconfigurability for response to dynamic events. Previous investigations regarding constellation reconfigurability consist of deterministic mission environments, in which the formulations consider a priori knowledge of the environment; however, observation objectives inherently involve uncertainty at any given time. As such, scheduling satellite operations must account for uncertainties to ensure adequate observation tasking. In response, we present a stochastic variant of the Multistage Constellation Reconfiguration Problem (MCRP) which is solved using Stochastic Dual Dynamic Integer Programming (SDDiP). We additionally explore other stochastic problem-solving techniques for solution quality comparison. To demonstrate each solution method, two computational experiments with stochastic target properties are conducted. The first concerns random orbital targets, and the second concerns simulated hurricanes. The results of the experiments demonstrate the effectiveness of the SDDiP solution approach over other stochastic problem-solving methods. Overall, the stochastic MCRP accounts for target stochasticity while obeying visible time windows and maneuver feasibility.

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arXiv2026

Decomposition-Based QAOA for Maximum Coverage Location Problem in Satellite Constellation Design

An increase in earth observation missions has increased the demand of efficient design and optimization of satellite constellations. Maximizing coverage of the target while effectively utilizing the limited orbital resources is one of the critical design challenges for complex combinatorial optimization problems. The maximal covering location problem (MCLP), serves as a base for orbital coverage modeling, is NP-hard and computationally intractable for large-constellation instances. Using heuristics, metaheuristics, and mixed-integer linear programming, classical solvers have achieved optimal or near-optimal results, yet their scalability is limited as the problem size increases. Quantum computing advancements, including the quantum approximate optimization algorithms, offer a potential solution to NP-hard combinatorial optimization problems. Current quantum hardware limitations, such as low qubit counts and circuit depth, restrict solutions for small-scale instance problems. To address this challenge, this paper proposes a scalable quantum optimization framework for MCLP in satellite constellation design. A decomposition-based quantum methodology is proposed, in which large MCLP instances are partitioned into subgraphs by classical decomposition, optimized independently via quantum optimization circuits, and combined using quantum reconstruction strategies. Computational results across different constellation sizes reveal better scalability in less time while maintaining competitive coverage performance compared to classical solvers.

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arXiv2026

Automating the Wildfire Detection and Scheduling Pipeline with Maneuverable Earth Observation Satellites

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 related 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 multisatellite 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 demonstrate that this autonomous detection and scheduling approach effectively enhances wildfire monitoring capabilities.

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arXiv2026

Optimal Satellite Constellation Configuration Design: A Collection of Mixed Integer Linear Programs

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.

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arXiv2025

Enhancing Orbital Debris Remediation with Reconfigurable Space-Based Laser Constellations

Orbital debris poses an escalating threat to space missions and the long-term sustainability of Earth's orbital environment. The literature proposes various approaches for orbital debris remediation, including the use of multiple space-based lasers that collaboratively engage debris targets. While the proof of concept for this laser-based approach has been demonstrated, critical questions remain about its scalability and responsiveness as the debris population continues to expand rapidly. This paper introduces constellation reconfiguration as a system-level strategy to address these limitations. Through coordinated orbital maneuvers, laser-equipped satellites can dynamically adapt their positions to respond to evolving debris distributions and time-critical events. We formalize this concept as the Reconfigurable Laser-to-Debris Engagement Scheduling Problem (R-L2D-ESP), an optimization framework that determines the optimal sequence of constellation reconfigurations and laser engagements to maximize debris remediation capacity, which quantifies the constellation's ability to nudge, deorbit, or perform just-in-time collision avoidance maneuvers on debris objects. To manage the complexity of this combinatorial optimization problem, we employ a receding horizon approach. Our experiments reveal that reconfigurable constellations significantly outperform static ones, achieving greater debris remediation capacity and successfully deorbiting substantially more debris objects. Additionally, our sensitivity analyses identify the key parameters that influence remediation performance the most, providing essential insights for future system design. These findings demonstrate that constellation reconfiguration represents a promising advancement for laser-based debris removal systems, offering the adaptability and scalability necessary to enhance this particular approach to orbital debris remediation.

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arXiv2025

Reconfigurable Earth Observation Satellite Scheduling Problem

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.

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arXiv2025

Space Logistics Analysis and Incentive Design for Commercialization of Orbital Debris Remediation

As orbital debris continues to become a higher priority for the space industry, there is a need to explore how partnerships between the public and private space sector may aid in addressing this issue. This research develops a space logistics framework for planning orbital debris remediation missions, providing a quantitative basis for partnerships that are mutually beneficial between space operators and debris remediators. By integrating network-based space logistics and game theory, we illuminate the high-level costs of remediating orbital debris, and the surplus that stands to be shared as a result. These findings indicate significant progress toward the continued development of a safe, sustainable, and profitable space economy.

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arXiv2025

Cislunar Satellite Constellation Design Via Integer Linear Programming

Cislunar space domain awareness is of increasing interest to the international community as Earth-Moon traffic is projected to increase, which raises the problem of placing space-based sensors optimally in a constellation to satisfy the space domain awareness demand in cislunar space. This demand profile can vary over space and time, making the design optimization problem challenging. This paper tackles the problem of satellite constellation design for spatio-temporally varying coverage demand by leveraging an integer linear programming formulation. The developed optimization formulation assumes the circular restricted 3-body dynamics and attempts to minimize the number of satellites required for the requested demand profile.

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arXiv2025

Optimal Placement and Coordinated Scheduling of Distributed Space-Based Lasers for Orbital Debris Remediation

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.

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arXiv2025

Distributed Space Resource Logistics Architecture Optimization under Economies of Scale

This paper proposes an optimization framework for distributed resource logistics system design to support future multimission space exploration. The performance and impact of distributed In-Situ Resource Utilization (ISRU) systems in facilitating space transportation are analyzed. The proposed framework considers technology trade studies, deployment strategy, facility location evaluation, and resource logistics after production for distributed ISRU systems. We develop piecewise linear sizing and cost estimation models based on economies of scale that can be easily integrated into network-based mission planning formulations. A case study on a multi-mission cislunar logistics campaign is conducted to demonstrate the value of the proposed method and evaluate key tradeoffs to compare the performance of distributed ISRU systems with traditional concentrated ISRU. Finally, a comprehensive sensitivity analysis is performed to assess the proposed system under varying conditions, comparing concentrated and distributed ISRU systems.

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arXiv2024

Benchmarking Agility and Reconfigurability in Satellite Systems for Tropical Cyclone Monitoring

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.

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arXiv2024

Embedded State Estimation for Optimization of Cislunar Space Domain Awareness Constellation Design

The traffic in cislunar space is expected to increase over the coming years, leading to a higher likelihood of conjunction events among active satellites, orbital debris, and non-cooperative satellites. This increase necessitates enhanced space domain awareness (SDA) capabilities that include state estimation for targets of interest. Both Earth surface-based and space-based observation platforms in geosynchronous orbit or below face challenges such as range, exclusion, and occlusion that hinder observation. Motivated by the need to place space-based observers in the cislunar space regime to overcome these challenges, this paper proposes a cislunar SDA constellation design and analysis framework that integrates state estimation into an optimization problem for determining the placement of observers for optimal state estimation performance on a set of targets. The proposed multi-observer placement optimization problem samples from a range of possible target orbits. Upon convergence, the optimized constellation is validated against a broader set of targets to assess its effectiveness. Two comparative analyses are presented to evaluate the effects of changes in the sensor tasking procedure and sensor fidelity on the optimized constellation, comparing these to a single observer baseline case. The results demonstrate that the optimized constellations can provide accurate state estimation for various orbit families.

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arXiv2024

Deterministic Multistage Constellation Reconfiguration Using Integer Programming and Sequential Decision-Making Methods

In this paper, we address the problem of reconfiguring Earth observation satellite constellation systems through multiple stages. The Multi-stage Constellation Reconfiguration Problem (MCRP) aims to maximize the total observation rewards obtained by covering a set of targets of interest through the active manipulation of the orbits and relative phasing of constituent satellites. In this paper, we consider deterministic problem settings in which the targets of interest are known a priori. We propose a novel integer linear programming formulation for MCRP, capable of obtaining provably optimal solutions. To overcome computational intractability due to the combinatorial explosion in solving large-scale instances, we introduce two computationally efficient sequential decision-making methods based on the principles of a myopic policy and a rolling horizon procedure. The computational experiments demonstrate that the devised sequential decision-making approaches yield high-quality solutions with improved computational efficiency over the baseline MCRP. Finally, a case study using Hurricane Harvey data showcases the advantages of multi-stage constellation reconfiguration over single-stage and no-reconfiguration scenarios.

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arXiv2023

Regional Constellation Reconfiguration Problem: Integer Linear Programming Formulation and Lagrangian Heuristic Method

A group of satellites, with either homogeneous or heterogeneous orbital characteristics and/or hardware specifications, can undertake a reconfiguration process due to variations in operations pertaining to Earth observation missions. This paper investigates the problem of optimizing a satellite constellation reconfiguration process against two competing mission objectives: (i) the maximization of the total coverage reward and (ii) the minimization of the total cost of the transfer. The decision variables for the reconfiguration process include the design of the new configuration and the assignment of satellites from one configuration to another. We present a novel bi-objective integer linear programming formulation that combines constellation design and transfer problems. The formulation lends itself to the use of generic mixed-integer linear programming (MILP) methods such as the branch-and-bound algorithm for the computation of provably-optimal solutions; however, these approaches become computationally prohibitive even for moderately-sized instances. In response to this challenge, this paper proposes a Lagrangian relaxation-based heuristic method that leverages the assignment problem structure embedded in the problem. The results from the computational experiments attest to the near-optimality of the Lagrangian heuristic solutions and a significant improvement in the computational runtime compared to a commercial MILP solver.

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arXiv2020

Satellite Constellation Pattern Optimization for Complex Regional Coverage

The use of regional coverage satellite constellations is on the rise, urging the need for an optimal constellation design method for complex regional coverage. Traditional constellations are often designed for continuous global coverage, and the few existing regional constellation design methods lead to suboptimal solutions for periodically time-varying or spatially-varying regional coverage requirements. This paper introduces a new general approach to design an optimal constellation pattern that satisfies such complex regional coverage requirements. To this end, the circular convolution nature of the repeating ground track orbit and common ground track constellation is formalized. This formulation enables a scalable constellation pattern analysis for multiple target areas and with multiple sub-constellations. The formalized circular convolution relationship is first used to derive a baseline constellation pattern design method with the conventional assumption of symmetry. Next, a novel method based on binary integer linear programming is developed, which aims to optimally design a constellation pattern with the minimum number of satellites. This binary integer linear programming method is shown to achieve optimal constellation patterns for general problem settings that the baseline method cannot achieve. Five illustrative examples are analyzed to demonstrate the value of the proposed new approach.

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