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Uncertainty-Based Methodology for the Development of Space Domain Awareness Architectures in Three-Body Regimes
Uncertainty-Based Methodology for the Development of Space Domain Awareness Architectures ...
Uncertainty-Based Methodology for the Development of Space Domain Awareness Architectures in Three-Body Regimes

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자료유형  
 학위논문 서양
최종처리일시  
20260202105516
ISBN  
9798263337957
DDC  
300
저자명  
Gilmartin, Matthew.
서명/저자  
Uncertainty-Based Methodology for the Development of Space Domain Awareness Architectures in Three-Body Regimes
발행사항  
[Sl] : Georgia Institute of Technology, 2024
발행사항  
Ann Arbor : ProQuest Dissertations & Theses, 2024
형태사항  
280 p
주기사항  
Source: Dissertations Abstracts International, Volume: 87-06, Section: B.
주기사항  
Advisor: Mavris, Dimitri.
학위논문주기  
Thesis (Ph.D.)--Georgia Institute of Technology, 2024.
초록/해제  
요약The past decade has seen a massive growth in interest in lunar space exploration. An increase in global competition has led a growing number of countries and non-governmental organizations towards lunar space exploration as a means to demonstrate their industrial and technological capabilities. This increase in cislunar space activity and resulting increase congestion and conjunction events poses a significant safety impacts to spacecraft on or around the moon. This risk was demonstrated on October 18th 2021 when India's Chandrayaan 2 orbiter was forced to maneuver to avoid a collision with NASA's Lunar Reconnaissance Orbiter. In order to mitigate the safety impacts of increased congestion, enhanced space traffic management capabilities are needed in the cislunar regime. One foundational component of space traffic management is space domain awareness (SDA). Current SDA infrastructure, a network of earth-based and space-based sensors, was designed to track objects in near-earth orbits, and is not suitable for tracking objects in distant, non-Keplerian cislunar orbits. As a result, new infrastructure is needed to fill this capability gap.The cislunar regime presents a number of challenges and constraints that complicate the SDA architecture design space. Unlike the near-earth regime, cislunar space is a three-body environment, violating many of the simplifying assumptions and models that are used in the near-earth domain. Furthermore, instability in cislunar dynamics means that state uncertainty plays a much more dominant role in system performance. This research identified three technology gaps exposed by the transition to the cislunar regime, that impede the ability of designers to explore the design space and perform many-query analyses, such as design optimization. A new uncertainty-based methodology was then proposed to both address these gaps and enhance design space exploration.The first technology gap identified was a reliance on three-body dynamics violate analytic two-body models of spacecraft motion, meaning that cislunar trajectories must be numerically integrated at much greater computational cost. A method was proposed that combines surrogate modeling techniques with and orbit family approach to develop an analytic parametric model of spacecraft motion. An experiment was carried out in order to interrogate the efficacy of this approach. Multiple surrogate models were generated using the approach, and each was compared to the state-of-the-art numerical integration approach. The surrogate modeling approach was found to greatly reduce the computational cost required to determine the initial state of an arbitrary periodic cislunar trajectory, while maintaining comparable accuracy to existing full-order methods. Of the surrogate model formulations tested, the interpolation methods were found to have the best combination of accuracy and speed for the proposed application.The second technology gap identified was a reliance on Gaussian distributions in most tracking filter implementations. In non-linear domains such as the cislunar regime Gaussian distributions may deviate from a Gaussian shape when propagated through the system's non-linear dynamics. This creates convergence issues that limit the robustness of tracking schemes that rely on Gaussian characterizations of uncertainty. This in turn creates a need to characterize the realism of Gaussian uncertainty approximations of potentially non-Gaussian uncertainty distributions. The characterization of uncertainty realism was identified to be a computationally intensive process, limiting the breadth of potential design space exploration. To ameliorate this issue a surrogate modeling process was proposed for the development of models to characterize the realism of uncertainty estimates produced by tracking filters. An experiment was executed to evaluate the efficacy of this approach. The surrogate modeling process was found to greatly improve on the computational cost of the full-order analysis. While the surrogate models were found to have non-negligible errors, these errors were on the same order of magnitude as the variability of the full-order model. Of the models tested, the model based on boosted decision trees was found to have the best balance of speed and accuracy. This massive increase in computational efficiency enables designers to evaluate much larger volumes of design cases using the same hardware.The third identified technology gap was the exponential increases in the computational cost required to evaluate tracking uncertainty using full-order cislunar SDA simulations, as the number and diversity of systems in an SDA system increases. As a result of this ballooning computational cost, detailed uncertainty quantification can rapidly become intractable in a many-query analysis context, limiting the scope of design space exploration and uncertainty quantification. A surrogate modeling method was proposed to provide a volumetric assessment of tracking performance at reduce the computational cost compared to existing methods. As part of this proposed approach, changes in tracking uncertainty were evaluated with respect to the search volume. Changes in uncertainty were evaluated using a novel equivalent radius metric to estimate the rate of information gain of information gain for individual sensor systems which is then aggregated for the overall architecture. As part of this approach, field surrogates and reduced order models were investigated as potential techniques to improve the computational cost and quality of the generated surrogate models. An experiment was performed to investigate the efficacy of the proposed method in comparison to the existing methods. The generated surrogate models were found to significantly reduce the computational cost of the tracking analysis. Furthermore, this experiment found scalar surrogate models to provide the most accurate modeling of the full-order models. The field surrogates generally under-performed their scalar counterparts in terms of goodness-of-fit. Of the models tested, the scalar boosted decision tree model was found to have the best balance of speed and accuracy. In practice, this model offered was able to reduce the computational cost of evaluating SDA architecture tracking performance by several orders of magnitude, enabling designers to increase the breadth of design space exploration by similar orders of magnitude.Finally, each of the developed modeling approaches were integrated into a unified methodology, named VENATOR, to evaluate Space Domain Awareness (SDA) architectures. A demonstration experiment was proposed, wherein the proposed VENATOR uncertainty-based methodology was compared to a state-of-the-art methodology using equivalent full-order analyses. The experiment was broken into two phases. In the first phase, both frameworks were used to evaluate the same architecture. Next, in the second phase, the VENATOR uncertainty-based methodology was used to evaluate a simple optimization problem. The first phase of this analysis found the VENATOR uncertainty-based methodology to offer an improvement in computational cost of over three orders of magnitude. During the second phase, a simple optimization was run using the VENATOR uncertainty-based methodology, evaluating over 82,000 cases in a total of 1.6 days. A short design space exploration was carried out, identifying the Pareto front of non-dominated cases, to demonstrate the utility of this approach. Using the run time of the state-of-the-art system when evaluating a single architecture, it was estimated that using this reference methodology would have taken over 14 years to evaluate the same number of cases using the same hardware. This massive increase in computational efficiency allows for designers to greatly increase the breadth of design space exploration, enabling them to examine far larger case loads, reducing design risk and increasing design knowledge. For this reason the uncertainty-based methodology was deemed to be a significant improvement over the state-of-the-art methodologies.
일반주제명  
Monte Carlo simulation
일반주제명  
Realism
일반주제명  
Visualization
일반주제명  
Aerospace engineering
키워드  
Space domain awareness
키워드  
Body dynamics
기타저자  
Georgia Institute of Technology.
기본자료저록  
Dissertations Abstracts International. 87-06B.
전자적 위치 및 접속  
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■1001  ▼aGilmartin,  Matthew.
■24510▼aUncertainty-Based  Methodology  for  the  Development  of  Space  Domain  Awareness  Architectures  in  Three-Body  Regimes
■260    ▼a[Sl]▼bGeorgia  Institute  of  Technology▼c2024
■260  1▼aAnn  Arbor▼bProQuest  Dissertations  &  Theses▼c2024
■300    ▼a280  p
■500    ▼aSource:  Dissertations  Abstracts  International,  Volume:  87-06,  Section:  B.
■500    ▼aAdvisor:  Mavris,  Dimitri.
■5021  ▼aThesis  (Ph.D.)--Georgia  Institute  of  Technology,  2024.
■520    ▼aThe  past  decade  has  seen  a  massive  growth  in  interest  in  lunar  space  exploration.  An  increase  in  global  competition  has  led  a  growing  number  of  countries  and  non-governmental  organizations  towards  lunar  space  exploration  as  a  means  to  demonstrate  their  industrial  and  technological  capabilities.  This  increase  in  cislunar  space  activity  and  resulting  increase  congestion  and  conjunction  events  poses  a  significant  safety  impacts  to  spacecraft  on  or  around  the  moon.  This  risk  was  demonstrated  on  October  18th  2021  when  India's  Chandrayaan  2  orbiter  was  forced  to  maneuver  to  avoid  a  collision  with  NASA's  Lunar  Reconnaissance  Orbiter.  In  order  to  mitigate  the  safety  impacts  of  increased  congestion,  enhanced  space  traffic  management  capabilities  are  needed  in  the  cislunar  regime.  One  foundational  component  of  space  traffic  management  is  space  domain  awareness  (SDA).  Current  SDA  infrastructure,  a  network  of  earth-based  and  space-based  sensors,  was  designed  to  track  objects  in  near-earth  orbits,  and  is  not  suitable  for  tracking  objects  in  distant,  non-Keplerian  cislunar  orbits.  As  a  result,  new  infrastructure  is  needed  to  fill  this  capability  gap.The  cislunar  regime  presents  a  number  of  challenges  and  constraints  that  complicate  the  SDA  architecture  design  space.  Unlike  the  near-earth  regime,  cislunar  space  is  a  three-body  environment,  violating  many  of  the  simplifying  assumptions  and  models  that  are  used  in  the  near-earth  domain.  Furthermore,  instability  in  cislunar  dynamics  means  that  state  uncertainty  plays  a  much  more  dominant  role  in  system  performance.  This  research  identified  three  technology  gaps  exposed  by  the  transition  to  the  cislunar  regime,  that  impede  the  ability  of  designers  to  explore  the  design  space  and  perform  many-query  analyses,  such  as  design  optimization.  A  new  uncertainty-based  methodology  was  then  proposed  to  both  address  these  gaps  and  enhance  design  space  exploration.The  first  technology  gap  identified  was  a  reliance  on  three-body  dynamics  violate  analytic  two-body  models  of  spacecraft  motion,  meaning  that  cislunar  trajectories  must  be  numerically  integrated  at  much  greater  computational  cost.  A  method  was  proposed  that  combines  surrogate  modeling  techniques  with  and  orbit  family  approach  to  develop  an  analytic  parametric  model  of  spacecraft  motion.  An  experiment  was  carried  out  in  order  to  interrogate  the  efficacy  of  this  approach.  Multiple  surrogate  models  were  generated  using  the  approach,  and  each  was  compared  to  the  state-of-the-art  numerical  integration  approach.  The  surrogate  modeling  approach  was  found  to  greatly  reduce  the  computational  cost  required  to  determine  the  initial  state  of  an  arbitrary  periodic  cislunar  trajectory,  while  maintaining  comparable  accuracy  to  existing  full-order  methods.  Of  the  surrogate  model  formulations  tested,  the  interpolation  methods  were  found  to  have  the  best  combination  of  accuracy  and  speed  for  the  proposed  application.The  second  technology  gap  identified  was  a  reliance  on  Gaussian  distributions  in  most  tracking  filter  implementations.  In  non-linear  domains  such  as  the  cislunar  regime  Gaussian  distributions  may  deviate  from  a  Gaussian  shape  when  propagated  through  the  system's  non-linear  dynamics.  This  creates  convergence  issues  that  limit  the  robustness  of  tracking  schemes  that  rely  on  Gaussian  characterizations  of  uncertainty.  This  in  turn  creates  a  need  to  characterize  the  realism  of  Gaussian  uncertainty  approximations  of  potentially  non-Gaussian  uncertainty  distributions.  The  characterization  of  uncertainty  realism  was  identified  to  be  a  computationally  intensive  process,  limiting  the  breadth  of  potential  design  space  exploration.  To  ameliorate  this  issue  a  surrogate  modeling  process  was  proposed  for  the  development  of  models  to  characterize  the  realism  of  uncertainty  estimates  produced  by  tracking  filters.  An  experiment  was  executed  to  evaluate  the  efficacy  of  this  approach.  The  surrogate  modeling  process  was  found  to  greatly  improve  on  the  computational  cost  of  the  full-order  analysis.  While  the  surrogate  models  were  found  to  have  non-negligible  errors,  these  errors  were  on  the  same  order  of  magnitude  as  the  variability  of  the  full-order  model.  Of  the  models  tested,  the  model  based  on  boosted  decision  trees  was  found  to  have  the  best  balance  of  speed  and  accuracy.  This  massive  increase  in  computational  efficiency  enables  designers  to  evaluate  much  larger  volumes  of  design  cases  using  the  same  hardware.The  third  identified  technology  gap  was  the  exponential  increases  in  the  computational  cost  required  to  evaluate  tracking  uncertainty  using  full-order  cislunar  SDA  simulations,  as  the  number  and  diversity  of  systems  in  an  SDA  system  increases.  As  a  result  of  this  ballooning  computational  cost,  detailed  uncertainty  quantification  can  rapidly  become  intractable  in  a  many-query  analysis  context,  limiting  the  scope  of  design  space  exploration  and  uncertainty  quantification.  A  surrogate  modeling  method  was  proposed  to  provide  a  volumetric  assessment  of  tracking  performance  at  reduce  the  computational  cost  compared  to  existing  methods.  As  part  of  this  proposed  approach,  changes  in  tracking  uncertainty  were  evaluated  with  respect  to  the  search  volume.  Changes  in  uncertainty  were  evaluated  using  a  novel  equivalent  radius  metric  to  estimate  the  rate  of  information  gain  of  information  gain  for  individual  sensor  systems  which  is  then  aggregated  for  the  overall  architecture.  As  part  of  this  approach,  field  surrogates  and  reduced  order  models  were  investigated  as  potential  techniques  to  improve  the  computational  cost  and  quality  of  the  generated  surrogate  models.  An  experiment  was  performed  to  investigate  the  efficacy  of  the  proposed  method  in  comparison  to  the  existing  methods.  The  generated  surrogate  models  were  found  to  significantly  reduce  the  computational  cost  of  the  tracking  analysis.  Furthermore,  this  experiment  found  scalar  surrogate  models  to  provide  the  most  accurate  modeling  of  the  full-order  models.  The  field  surrogates  generally  under-performed  their  scalar  counterparts  in  terms  of  goodness-of-fit.  Of  the  models  tested,  the  scalar  boosted  decision  tree  model  was  found  to  have  the  best  balance  of  speed  and  accuracy.  In  practice,  this  model  offered  was  able  to  reduce  the  computational  cost  of  evaluating  SDA  architecture  tracking  performance  by  several  orders  of  magnitude,  enabling  designers  to  increase  the  breadth  of  design  space  exploration  by  similar  orders  of  magnitude.Finally,  each  of  the  developed  modeling  approaches  were  integrated  into  a  unified  methodology,  named  VENATOR,  to  evaluate  Space  Domain  Awareness  (SDA)  architectures.  A  demonstration  experiment  was  proposed,  wherein  the  proposed  VENATOR  uncertainty-based  methodology  was  compared  to  a  state-of-the-art  methodology  using  equivalent  full-order  analyses.  The  experiment  was  broken  into  two  phases.  In  the  first  phase,  both  frameworks  were  used  to  evaluate  the  same  architecture.  Next,  in  the  second  phase,  the  VENATOR  uncertainty-based  methodology  was  used  to  evaluate  a  simple  optimization  problem.  The  first  phase  of  this  analysis  found  the  VENATOR  uncertainty-based  methodology  to  offer  an  improvement  in  computational  cost  of  over  three  orders  of  magnitude.  During  the  second  phase,  a  simple  optimization  was  run  using  the  VENATOR  uncertainty-based  methodology,  evaluating  over  82,000  cases  in  a  total  of  1.6  days.  A  short  design  space  exploration  was  carried  out,  identifying  the  Pareto  front  of  non-dominated  cases,  to  demonstrate  the  utility  of  this  approach.  Using  the  run  time  of  the  state-of-the-art  system  when  evaluating  a  single  architecture,  it  was  estimated  that  using  this  reference  methodology  would  have  taken  over  14  years  to  evaluate  the  same  number  of  cases  using  the  same  hardware.  This  massive  increase  in  computational  efficiency  allows  for  designers  to  greatly  increase  the  breadth  of  design  space  exploration,  enabling  them  to  examine  far  larger  case  loads,  reducing  design  risk  and  increasing  design  knowledge.  For  this  reason  the  uncertainty-based  methodology  was  deemed  to  be  a  significant  improvement  over  the  state-of-the-art  methodologies.
■590    ▼aSchool  code:  0078.
■650  4▼aMonte  Carlo  simulation
■650  4▼aRealism
■650  4▼aVisualization
■650  4▼aAerospace  engineering
■653    ▼aSpace  domain  awareness
■653    ▼aBody  dynamics
■690    ▼a0538
■71020▼aGeorgia  Institute  of  Technology.
■7730  ▼tDissertations  Abstracts  International▼g87-06B.
■790    ▼a0078
■791    ▼aPh.D.
■792    ▼a2024
■793    ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360386▼nKERIS▼z이  자료의  원문은  한국교육학술정보원에서  제공합니다.

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