modeling and optimization of space networks to improve
TRANSCRIPT
Modeling and Optimization of Space Networks to Improve
Communication Capacity
PhD Defense
Sara Spangelo
Motivation
Contributions
Modeling
Assessment
Optimization
Doctoral Committee:
Professor James W. Cutler, Chair
Professor Amy M. Cohn
Professor Dennis S. Bernstein
Professor Ella M. Atkins
Aerospace Engineering
University of Michigan
Dec. 18, 2012
Optimization
Algorithms
Applications
Conclusion
Future Work
1
Why small satellites (1-10 kg)?
• Mission applications:
•Science: in-situ, remote sensing [1]
•Tech demos, precursor missions [2]
• Easy access to space (secondaries) [1]
• Fast, cheap (<1 year, ~$1 M) [3]
• Educational opportunities
Motivation
Contributions
Modeling
Assessment
Optimization
Motivation: Small Satellites and Experience
RAX-1 (2010-2011) RAX-2 (2011-present)
University of Michigan CubeSat launches:
• RAX-1 (2010-2011), RAX-2 (2011-present)
• Download to global ground network
• RAX team: experienced lifecycles, failures, recoveries
2
Optimization
Algorithms
Applications
Conclusion
Future Work
RAX-1 (2010-2011) RAX-2 (2011-present)
Yagi Antenna, FXB roof, University of Michigan
[1] Moretto et al., 2008, [2] Buck et al., 2012, [3] Baker et al., 2008Image credit: Michigan
Exploration Labs
Motivation
Contributions
Modeling
Assessment
Optimization
Motivation: Feeling the Pain
Cause EffectSmall satellite constraints [2,3] volume, mass, power, coste.g. 1U CubeSat =10cm3, 1kg, <3W, <$1M,“free” launches as secondaries
• Integration challenges • Limited energy to support operations• Limited on-board data/ energy storage• Far more data collected than can be downloaded• Thermal concerns• No control over orbit
Ground Network: low-cost, independently owned [4]
• Limited/ unknown availability • Stochastic efficiency of stations
3
Optimization
Algorithms
Applications
Conclusion
Future Work
University institutions, new space and ground systems
• Must develop new models/ simulators• Challenges integrating simulation/ planning tools• Every day we need a schedule!
Growing community: ElaNa launches (33 in 2013-4), constellation missions [4]
• This is going to get increasingly difficult/ complex!
[2] Buck et al., 2012, [3] Baker et al., 2008, [4] Cutler et al., 2006, [5] Ridley et al., 2010
Summary: Operating small satellites with low-cost networks is challenging!
Thesis Goals and Contributions
Goal:
Develop a generic, modular, analytical modeling and simulation framework to enable:
1) assessment and scheduling optimization of current missions
2) improve the design of future missions and ground networks
Thesis Contributions
Motivation
Contributions
Modeling
Assessment
Optimization
Image Credit: Michigan Exploration Labs
4
1. Analytical modeling framework for space networks
• Generic templates for dynamics, constraints, and objectives
2. Constraint-based capacity assessment using models and simulators
• Assess energy and network constraints, enables requirement verification
3. Formulate and solve deterministic spacecraft scheduling optimization problems
• Applications to linear/ nonlinear, realistic/ generic problem instances
Optimization
Algorithms
Applications
Conclusion
Future Work
Existing Literature: Contributions and Drawbacks
Literature
Area
Contributions Drawbacks (relative to goals)
Space Links • Analytical visibility model [6],• Simulators for coverage [7], access time analysis, e.g. System Tool Kit (STK) [8-10]
• Models: not generic, extensible• Simulators: not integrated
Spacecraft Models/ Simulators
• High-fidelity (mission specific) [11]• Low-fidelity (subset of subsystem) [12]
• Not generic, modular, extensible• Simplified, neglects subsystem interactions
OptimizingSpacecraft Operations
• Earth Observing Satellite (EOS) [13-15]• Scheduling downlinks [16-18]
• Neglects coupling of dynamics/ constraints• Simplified models, small problems
Motivation
Contributions
Modeling
Assessment
Optimization
[6] Salmasi, 1983, [7] Beste, 1978, [8] AGI, 2012, [9] Beering et al., 2009, [10] Cutler et al., 2009, [11] McFadden et al., 2001, [12] Mosher et al., 1998,
[13] Vasquez et al., 2001, [14] Martin, 2012, [15] Lemaitre et al., 2002, [16] Barbulescu et al., 2004, [17] Fabrizio et al., 2005, [18] Cheung et al., 2002,
[19] Swamy et al., 2006, [20] Johnston, 2008, [21] Liao et al., 2005, [22] Smith et al., 1998, [23] Chien et al., 2001, [24] Dvorak et al., [25] Green et al., 1995.
5
StochasticScheduling
• Modeling distributions (PDFs) [19]• Deep Space Network (DSN) scheduling under uncertainty [20, 21]
• Limited stochastic satellite scheduling work• Neglects on-board dynamics/ constraints
Space Operations Architectures/ Frameworks
• ASPEN [22], CASPER [23]: actively used • MDS [24], MOS 2.0 [25]: systems engineering tools[Many common elements to our work]
• Uniquely for scheduling/ execution• Search algorithms: heuristic/ sub-optimal•MDS/ MOS: not yet deployed, lack scheduling capabilities (but can accommodate)
Optimization
Algorithms
Applications
Conclusion
Future Work
Summary: No general, analytical approach for operations and design.
Motivation
Contributions
Modeling
Assessment
Optimization
Elements
ParametersStatesSubsystemsSchedule
Modeling Framework: Analytical Formulation
6
Optimization
Algorithms
Applications
Conclusion
Future Work
Modeling Framework: Analytical Formulation
Motivation
Contributions
Modeling
Assessment
Optimization
Elements
Framework
ParametersStatesSubsystemsSchedule
7
Optimization
Algorithms
Applications
Conclusion
Future Work
X: States, N: Nominal dynamics, F: Functional dynamics, S: Subsystems, Tf : Time horizon
(1) Objective
(2) Dynamics
(3) Capacity Constraints
(4) Minimum Exchange
Sun/ Eclipse
Ground Stations
Target of Interest
Modeling Framework : Communication-focused model
Motivation
Contributions
Modeling
Assessment
Optimization
8
RAX-2: 410 x 810 km,
101.5o orbit
Optimization
Algorithms
Applications
Conclusion
Future Work
Modeling Framework : Communication-focused model
Motivation
Contributions
Modeling
Assessment
Optimization
ParametersStatesSubsystemsSchedule
Orbit, GS Location, Battery/ Data Buffer sizesOn-board energy, On-board data, Downloaded dataCommunication, Payload, Energy/ Data ManagementGoverns when/ how to perform operations
P
X
-
U
Elements
Framework
Objective & Constraints Dynamics
9
Optimization
Algorithms
Applications
Conclusion
Future Work
D: Disturbance Forces, E: State Estimates
Objective & Constraints Dynamics
Capacity: Amount of data downloaded across network in planning horizon.
Motivation
Contributions
Modeling
Assessment
Optimization
Communication Capacity
10
Optimization
Algorithms
Applications
Conclusion
Future Work
Ground station perspective, assuming orbiting spacecraft is available
Constant communication possible
Capacity: Amount of data downloaded across network in planning horizon.
Motivation
Contributions
Modeling
Assessment
Optimization
Communication Capacity
11
Optimization
Algorithms
Applications
Conclusion
Future Work
Ground station perspective, assuming orbiting spacecraft is available
Additive Constraints: Orbit and station locations, Minimum elevation
Capacity: Amount of data downloaded across network in planning horizon.
Motivation
Contributions
Modeling
Assessment
Optimization
Communication Capacity
12
Optimization
Algorithms
Applications
Conclusion
Future Work
Ground station perspective, assuming orbiting spacecraft is available
Additive Constraints: Ground station availability
Capacity: Amount of data downloaded across network in planning horizon.
Motivation
Contributions
Modeling
Assessment
Optimization
Communication Capacity
13
Ground station perspective, assuming orbiting spacecraft is available
Optimization
Algorithms
Applications
Conclusion
Future Work
Additive Constraints: Local noise, slewing , keyholing
Network constraints: function of download time between satellite and networkNetwork constraints: function of download time between satellite and network
InclinationMotivation
Contributions
Modeling
Assessment
Optimization
8 RAX-2 ground stations8 RAX-2 ground stations
Constraint-based Capacity Analysis: Network
14
Optimization
Algorithms
Applications
Conclusion
Future Work
Satellites in circular 500 km altitude orbits Footprints for ground stations from survey
[10] Cutler et al., 2009
Inclination
100 global ground stations100 global ground stations
Constraint-based Capacity Analysis: Network
Motivation
Contributions
Modeling
Assessment
Optimization
Network constraints: function of download time between satellite and networkNetwork constraints: function of download time between satellite and network
15
Optimization
Algorithms
Applications
Conclusion
Future Work
Satellites in circular 500 km altitude orbits Footprints for ground stations from survey
[10] Cutler et al., 2009
Constraint-based Capacity Analysis: Energy
Altitude
Energy constraints: representing the total available energy for operations, which is a function of eclipse time and power collection (when in the sun) Energy constraints: representing the total available energy for operations, which is a function of eclipse time and power collection (when in the sun)
Motivation
Contributions
Modeling
Assessment
Optimization
16
Satellites in circular polar orbits
Eclipse fraction: fraction of orbital period in eclipse
Optimization
Algorithms
Applications
Conclusion
Future Work
Note: The sharp spikes at days 141 and 317 are due to the Moon’s penumbra.
Constraint-based Capacity Analysis : Energy
Example RAX-2 mission scenario communicating with real network.
Maximum Eclipse (35% of orbit)
Motivation
Contributions
Modeling
Assessment
Optimization
17
Optimization
Algorithms
Applications
Conclusion
Future Work
Constraint-based Capacity Analysis: Energy
Example RAX-2 mission scenario communicating with real network.
Maximum Eclipse (35% of orbit)
Expected Power Collection: 5.5 W
Motivation
Contributions
Modeling
Assessment
Optimization
18
Dtotal=0.3 MBytes
Optimization
Algorithms
Applications
Conclusion
Future Work
Constraint-based Capacity Analysis: Energy
Example RAX-2 mission scenario communicating with real network.
Zero Eclipse (all sunlight)
Worst-Case Power Collection: 3W
Motivation
Contributions
Modeling
Assessment
Optimization
19
Dtotal=0.9 MBytes
Optimization
Algorithms
Applications
Conclusion
Future Work
Components SMSP-specific
Objective Maximize communication capacity (data downloaded)
States On-board energy and data
Subsystems Payload, Communication, Energy Collection and Management,Data Collection and Management, Bus
Decisions • How much data to download?• What option (ground station, data rate)?
SMSP: Problem Description
Motivation
Contributions
Modeling
Assessment
Optimization
SMSP: Single-Satellite Multi-Ground Station Scheduling Problem
20
• What option (ground station, data rate)?
Constraints • Opportunities:
• Orbit, GS locations, targets of interest, min elevations
• Battery and data buffer capacities
• Single communication link to ground station
Optimization
Algorithms
Applications
Conclusion
Future Work
SMSP: Problem Formulation
Motivation
Contributions
Modeling
Assessment
Optimization
SMSP: Single-Satellite Multi-Ground Station Scheduling Problem
Challenges: Continuous-time dynamics, Buffer constraints
Discretize into set of intervals (I)
21
Optimization
Algorithms
Applications
Conclusion
Future Work
GS 3
GS 2
GS 1
SMSP: Single-Satellite Multi-Ground Station Scheduling Problem
UCF: Under-Constrained Formulation
SMSP: Problem Formulation
Motivation
Contributions
Modeling
Assessment
Optimization
22
Optimization
Algorithms
Applications
Conclusion
Future Work
SMSP: Special Case of Linear Dynamics
Theorem 1: With linear dynamics, UCF guarantees a feasible (thus
optimal) solution to the continuous-time dynamics.
Single-interval instance of SMSP Motivation
Contributions
Modeling
Assessment
Optimization
23
Optimization
Algorithms
Applications
Conclusion
Future Work
Note: energy rate (Joules/ sec) = power (W), linear energy = constant power
Single-interval instance of SMSP
SMSP: Special Case of Linear Dynamics
Motivation
Contributions
Modeling
Assessment
Optimization
Theorem 1: With linear dynamics, UCF guarantees a feasible (thus
optimal) solution to the continuous-time dynamics.
24
Optimization
Algorithms
Applications
Conclusion
Future Work
SMSP: Special Case of Linear Dynamics
Realistic RAX instances: solve quickly (seconds)
Motivation
Contributions
Modeling
Assessment
Optimization
Computations on Intel Core i7 2.8 GHz with 8 GB memory using CPLEX 12.2 C++ API with optimality gap of 0.01%.
25
Number of Intervals (56 days): n1=2.5k, n2=7.5k, n3=13k (k = 103)
Optimization
Algorithms
Applications
Conclusion
Future Work
SMSP: Special Case of Linear Dynamics
Generic problem instances:
• Solve quickly (instances with # intervals ≤10,000 solve in <2 minutes)
• Limited branching: Pareto-dominance, limited coupling between intervals
Motivation
Contributions
Modeling
Assessment
Optimization
Computations on Intel Core i7 2.8 GHz with 8 GB memory using CPLEX 12.2 C++ API with optimality gap of 0.01%.
26
Optimization
Algorithms
Applications
Conclusion
Future Work
Are there any instances challenging to solve? Yes!
When two options have competing desirable characteristics.
When linear program (LP) relaxation yields a higher objective than the mixed integer program (MIP)
Computational Results:
SMSP: Fractional Cases Yielding Branching
Motivation
Contributions
Modeling
Assessment
Optimization Optimality Gap (Opt Gap):
• Large data sets (n=2,500) solve in <3 minutes with Opt Gap =1%
• Increasing Opt Gap: little impact on objective, only tightens upper bound!
27
(n)
Optimization
Algorithms
Applications
Conclusion
Future Work
Optimality Gap (Opt Gap):
difference between current solution (in branch-and-bound tree) and best solution (LP relaxation)
Computations on Intel Core i7 2.8 GHz with 8 GB memory using CPLEX 12.2 C++ API with optimality gap of 0.01%.
SMSP: Nonlinear Dynamics
Nonlinear dynamics:
Rate of energy/ data acquired/ consumed not constant during interval
Ik : set of original intervals k=0Motivation
Contributions
Modeling
Assessment
Optimization
28
Number of intervals = 2
Optimization
Algorithms
Applications
Conclusion
Future Work
SMSP: Nonlinear Dynamics
Nonlinear SMSP Algorithm (NLSA):
Solves UCF with updated set of intervals (Ik) each iteration (k)
Ik : set of current intervals k=1
NLSA:
Motivation
Contributions
Modeling
Assessment
Optimization
29
Number of intervals = 4
NLSA:
1. Solve UCF (Ik)
2. Check feasibility3. Split intervals4. If infeasible:
Go to Step 1Else: Exit
Feasible→ Certi>icate of Feasibility
Optimization
Algorithms
Applications
Conclusion
Future Work
SMSP: Nonlinear Dynamics
Design Issues for implementing NLSA:
1. Approach for checking feasibility/splitting interval
Anticipative Greedy Assign and Check Algorithm (AGACA)
2. Approach for deciding which intervals to check
Split All- find and split all infeasible intervals (fewer Nits)
Motivation
Contributions
Modeling
Assessment
Optimization
30
Optimization
Algorithms
Applications
Conclusion
Future Work
Theorem 2: NLSA yields a feasible (and optimal) solution in a finite number
of iterations for instances of SMSP with piece-wise linear dynamics.
Interval
SOSP: Extending SMSP
• SMSP may result infeasibilities/ sub-optimal use of resources
• Satellite Operational Scheduling Problem (SOSP):
• Operational and download decisions
• Ensures optimal allocation of energy/ data dynamics
Problem SMSP SOSP
Motivation
Contributions
Modeling
Assessment
Optimization
31
Decisions •When/how to download •When/how to download •When/how to perform payload operations
Application Problems where payload operations pre-specified
Problems where payload operations are not specified.
Optimization
Algorithms
Applications
Conclusion
Future Work
Applications: LEO CubeSat Missions
10 real-world CubeSat missions with diverse goals, payloads, networks
Motivation
Contributions
Modeling
Assessment
Optimization
SMSP SOSP
•When/how to download •When/how to download •When/how to perform payload operations
32
Optimization
Algorithms
Applications
Conclusion
Future Work
Photo Credit: Online CubeSat Team Websites
Note: For CADRE, we assume the operational decisions are related to the GPS operation (and the science occurs constantly).
Missions that aren’t feasible with SMSP
Motivation
Contributions
Modeling
Assessment
Optimization
Applications: LEO CubeSat Missions
SMSP SOSP
•When/how to download •When/how to download •When/how to perform payload operations
32
Optimization
Algorithms
Applications
Conclusion
Future Work
Photo Credit: Online CubeSat Team Websites
Note: For CADRE, we assume the operational decisions are related to the GPS operation (and the science occurs constantly).
Missions that exceed requirements
Motivation
Contributions
Modeling
Assessment
Optimization
Applications: LEO CubeSat Missions
SMSP SOSP
•When/how to download •When/how to download •When/how to perform payload operations
32
Optimization
Algorithms
Applications
Conclusion
Future Work
Photo Credit: Online CubeSat Team Websites
Note: For CADRE, we assume the operational decisions are related to the GPS operation (and the science occurs constantly).
Missions that don’t satisfy requirements
Motivation
Contributions
Modeling
Assessment
Optimization
Applications: LEO CubeSat Missions
SMSP SOSP
•When/how to download •When/how to download •When/how to perform payload operations
32
Optimization
Algorithms
Applications
Conclusion
Future Work
Photo Credit: Online CubeSat Team Websites
Note: For CADRE, we assume the operational decisions are related to the GPS operation (and the science occurs constantly).
SMSP: Sources of Stochasticity
Sources of Stochasticity in SMSP:
Motivation
Contributions
Modeling
Assessment
Optimization
Objective:
Example: Actual is less efficient than expected
ηio: download efficiency using option o during i
33
Optimization
Algorithms
Applications
Conclusion
Future Work
Expected
Actual
Dtotal=0.9 MBytes
Dtotal=0.5 MBytes
Expected:
Actual (η ~.6):
Constraints
SMSP: Sources of Stochasticity
Sources of Stochasticity in SMSP:
Motivation
Contributions
Modeling
Assessment
Optimization
Example: Less energy (δe+) is collected than expected
34
Optimization
Algorithms
Applications
Conclusion
Future Work
Expected
Actual
Infeasibilities
• RAX-2 Problem: SMSP & sufficient energy
• Optimal solution → download every opportunity
• 2 additional constraints:
• Schedule byte limit → limits selection of Tile parts
• Collect beacons before/ after → minimum elevation
RAX-2 CubeSat Scheduling Problem
Radio Aurora
Explorer (RAX)
CubeSat
Motivation
Contributions
Modeling
Assessment
Optimization
35
Optimization
Algorithms
Applications
Conclusion
Future Work
Photo Credit: Michigan Exploration Labs
Step 1: Identify missing file parts, select lists, ranges for download
Motivation
Contributions
Modeling
Assessment
Optimization
RAX-2 CubeSat Scheduling Problem
File parts0 10050
36
Optimization
Algorithms
Applications
Conclusion
Future Work
Motivation
Contributions
Modeling
Assessment
Optimization
Step 2: Identify download opportunities (times above minimum elevations)
RAX-2 CubeSat Scheduling Problem
37
Optimization
Algorithms
Applications
Conclusion
Future Work
SRB, Ann Arbor, Michigan
Wellington, New Zealand
Tokyo, Japan
Menlo Park, California
Adelaide, Australia
Bolder, Colorado
Motivation
Contributions
Modeling
Assessment
Optimization
Step 3: Assign file parts to download opportunities
RAX-2 CubeSat Scheduling Problem
38
Optimization
Algorithms
Applications
Conclusion
Future Work
Motivation
Contributions
Modeling
Assessment
Optimization
Step 4: Upload and execute schedule, downloads to global network
Step 5: Stations collect data, use client to relay info back to RAX-2 ops team
RAX-2 CubeSat Scheduling Problem
39
Optimization
Algorithms
Applications
Conclusion
Future Work
Motivation
Contributions
Modeling
Assessment
Optimization
Step 6: Compute efficiency statistics (GS, elevation, time, etc.)
RAX-2 CubeSat Scheduling Problem
Be
fore
Aft
er
40
Optimization
Algorithms
Applications
Conclusion
Future Work
Data: Property of Michigan Exploration Labs
Aft
er
SRB, Ann
Arbor,
MI Ground
Station
Stochasticity in Objective ηio: download efficiency using option o during i
SRB, Ann
Arbor,
MI Ground
Station
Impact of Stochasticity on SMSP Solutions
Motivation
Contributions
Modeling
Assessment
Optimization
Data: Property of Michigan Exploration Labs
Representing stochastic efficiency data with
probability distribution functions (PDFs)
41
Optimization
Algorithms
Applications
Conclusion
Future Work
Stochasticity in Objective
Motivation
Contributions
Modeling
Assessment
Optimization
ηio: download efficiency using option o during i
Impact of Stochasticity on SMSP Solutions
42
Impact on distribution of solutions
(10,000 Monte Carlo runs)
Optimization
Algorithms
Applications
Conclusion
Future Work
1. Analytical modeling formulation for space systems
• Addressed need for modular, extensible, generic approach
• Provides foundational model for diverse missions
2. Constraint-based communication capacity
• Quantified network and energy constraints
• Assessed feasibility, identified excess and deficient resources
Conclusion
Motivation
Contributions
Modeling
Assessment
Optimization
3. Optimization formulations and algorithms
• Linear real-world and generic problem instances solve quickly
• Derived theoretical conditions for branching, investigated computational tractability
• Formulations result in significant improvement relative to requirements
• Models lay groundwork for design trades and sensitivity analysis
43
Photo Credit: Allison Craddock
Optimization
Algorithms
Applications
Conclusion
Future Work
1. Operational Planning for Complex Spacecraft
• Accommodate new states, subsystems, constraints, etc.
2. Multi-Satellite Missions (constellation, inter-sat links, formation flying, Mothership)
• New elements: network availability, conflicts, priority, cost constraints
3. Stochasticity in Operational Scheduling
• Investigate impact on feasibility/ performance
Future Work
Motivation
Contributions
Modeling
Assessment
Optimization• Investigate impact on feasibility/ performance
• Manage impact of uncertainty a priori and dynamically
4. Coupled Vehicle and Operations Optimization
• Simultaneously optimize vehicle, network, and operational decisions
5. Applications to Interplanetary Missions
• New challenges: access to DSN/ orbiters, financial cost, conflict resolution, long ranges
(transmission times, low data rates), limited uplink opportunities
44
Photo Credits: NSF website and Spangelo et al., iCubeSat 2012
Optimization
Algorithms
Applications
Conclusion
Future Work
Questions?
Acknowledgments
• Supportive Friends & Family for their encouragement
• Advisors: Prof. Cutler and Prof. Cohn for their tireless efforts
• Committee: Prof. Atkins, Prof. Bernstein for their support and input
• Prof. Gilbert for his encouragement, time, and life lessons
• Kyle Gilson, John Springann, Michigan Exploration Labs (MXL)• Kyle Gilson, John Springann, Michigan Exploration Labs (MXL)
• Radio Aurora eXplorer (RAX) Team
• CubeSat and Amateur Radio Communities
• National Science Foundation (NSF)
• National Science and Engineering Research Council of Canada (NSERC)
• University of Michigan Aerospace Engineering Department
Full References [1] Buck, J., “NASA Announces Third Round Of CubeSat Space Mission Candidates”, NASA Release RELEASE : 12-050,
http://www.nasa.gov/home/hqnews/2012/feb/HQ_12-050_CubeSats.html
[2] Baker, D. N. and Worden, S. P., “The Large Benefits of Small-Satellite Missions,” Transactions American
Geophysical Union, Vol. 89, No. 33, Aug. 2008, pp. 301.
[3] Moretto, T., “CubeSat Mission to Investigate Ionospheric Irregularities,” Space Weather, Vol. 6, No. 11, 2008.
[4] Cutler, J. and Fox, A., “A Framework for Robust and Flexible Ground Station Networks,” AIAA Journal of Aerospace
Computing, Information, and Communication, Vol. 3, March 2006, pp. 73–92.
[5] Ridley, A., Forbes, J., Cutler, J., Nicholas, A., Thayer, J., Fuller-Rowell, T., Matsuo, T., Bristow, W., Conde, M., Drob, D.,
Paxton, L., Chappie, S., Osborn, M., Dobbs, M., Roth, J., and Armada Mission Team, “The Armada mission: Determining
the dynamic and spatial response of the thermosphere/ionosphere system to energy inputs on global and regional
scales,” American Geophysical Union (AGU) Fall Meeting, Dec. 2010, pp. A7.
[6] Salmasi, A. and Rahmat-Samii, Y., “Beam Area Determination for Multiple-Beam Satellite Communication
Applications,” IEEE Trans. Aerosp. Electron. Syst, Vol. AES-19, No. 3, May 1983, pp. 405 –412.
[7] Beste, D., “Design of Satellite Constellations for Optimal Continuous Coverage,” IEEE Trans. Aerosp.
Electron. Syst, Vol. AES-14, No. 3, May 1978, pp. 466 –473.Electron. Syst, Vol. AES-14, No. 3, May 1978, pp. 466 –473.
[8] Analytics Graphics, Incorporated, “Satellite Tool Kit (STK),” 2012, http://www.stk.com/.
[9] Beering, D., Tseng, S., Hayden, J., Corder, A., Ooi, T., Elwell, D., Grabowski, H., Frederic, R., Franks, J., Fish, R.,
Johnson, A., and Gavin, N., “RF Communication Data Model for Satellite Networks,” IEEE Military Communications
Conference, Piscataway, NJ, USA, 2009, p. 7.
[10] Cutler, J. and Boone, D., “Assessing Global Ground Station Capacity,” CubeSat Developers’ Workshop, April 2009.
[11] McFadden, J., Ergun, R., Carlson, C., Herrick, W., Loran, J., Vernetti, J., Teitler, W., Bromund, K., and Quinn, T.,
“Science Operations and Data Handling for the FAST Satellite,” Space Science Reviews, Vol. 98, No. 1-2, 2001, pp. 169
– 96.
[12] Mosher, T., Barrera, M., Bearden, D., and Lao, N., “Integration of Small Satellite Cost and Design Models for
Improved Conceptual Design-to-Cost,” IEEE Aerospace Conference, Vol. 3, New York, NY, USA, 1998, pp. 97 – 103.
[13] Vasquez, M. and Hao, J., “A Logic-Constrained Knapsack Formulation and a Tabu Algorithm for the Daily
Photograph Scheduling of an Earth Observation Satellite,” Computational Optimization and Applications, Vol. 20,
2001, pp. 137–157.
Full References [14] Martin, 2012, Martin, W., “Satellite image collection optimization,” Optical Engineering, Vol. 41, No. 9, 2002,
pp. 2083 – 2087.
[15] Lemaitre, M., Verfaillie, G., Jouhaud, F., Lachiver, J.-M., and Bataille, N., “Selecting and scheduling observations
of agile satellites,” Aerospace Science and Technology, Vol. 6, No. 5, Sept. 2002, pp. 367 – 81.
[16] Barbulescu, L., Watson, J.-P., Whitley, L., and Howe, A., “Scheduling space-ground Communications for the Air
Force satellite control network,” Journal of Scheduling, Vol. 7, 2004, pp. 7–34.
[17] Fabrizio Marinelli, Salvatore Nocella, Fabrizio Rossi, and Stefano Smriglio. A Lagrangian Heuristic for Satellite
Range Scheduling with Resource Constraints. In Dipartimento di Informatica, Universitita degli Studi di L’Aquila,
Technical Report TRCS 004/2005, 2005.
[18] Cheung, K., Lee, C., Gearhart, W., Vo, T., and Sindi, S., “Link-capability driven network planning and operation,”
Vol. 7, Piscataway, NJ, USA, 2002, p. 3281.
[19] Johnston, M. D., “Deep Space Network Scheduling Using Multi-Objective Optimization With Uncertainty,”
SpaceOps Conference, 2009.
[19] Swamy, C. and Shmoys, D. B., “Approximation algorithms for 2-stage stochastic optimization problems,”
SIGACT News, Vol. 37, 2006, pp. 33–46.SIGACT News, Vol. 37, 2006, pp. 33–46.
[20] Johnston, M. D., “Deep Space Network Scheduling Using Multi-Objective Optimization With Uncertainty,”
SpaceOps Conference, 2008.
[21] Liao, D.-Y. and Yang, Y.-T., “Satellite imaging order scheduling with stochastic weather condition forecast,”
Systems, Man and Cybernetics, 2005 IEEE International Conference on, Vol. 3, Oct. 2005, pp. 2524 – 2529 Vol. 3.
[22] Smith, B., Sherwood, R., Govindjee, A., Yan, D., Rabideau, G., Chien, S., and Fukunaga, A., “Representing
Spacecraft Mission Planning Knowledge in ASPEN,” Artificial Intelligence Planning Systems Workshop on
Knowledge Acquisition, 1998.
[23] Chien, S., Knight, R., and Rabideau, G., “CASPER: using local search for planning for embedded systems,”
ESTEC Meeting on Onboard Autonomy, Noordwijk, The Netherlands, October 2001.
[24] Dvorak, D., Rasmussen, R., Reeves, G., and Sacks, A., “Software Architecture Themes in JPLs Mission Data
System,” IEEE Aerospace Conference, Big Sky, MT, March 2000.
[25] Green, W. B., “Multimission ground data system support of NASA’S planetary program,” Acta Astronautica, Vol.
37, No. 0, 1995, pp. 407 – 415.
Journal Publications
1. S. Spangelo, J. Cutler, A. Klesh, and D. Boone,“Models and Tools to Evaluate Space Communication Network Capacity”, IEEE
Transactions on Aerospace and Electronic Systems, July 2012.
2. S. Spangelo and E. Gilbert, “Power Optimization of Solar-Powered Aircraft with Specified Closed Ground Tracks”, Accepted to
Journal of Aircraft, May 2012.
3. S.C. Spangelo, M.W. Bennett, D.C. Meinzer, A.T. Klesh, J.A. Arlas, J.W. Cutler, “Design and Implementation of the GPS Subsystem for
the Radio Aurora Explorer”, Accepted to Acta Astronautica, December 2012.
4. D. Dalle and S. Spangelo, “Preliminary Design of Small Satellites for Passive Reentry”, Under review in Journal of Small Satellites
(JOSS).
5. S. Spangelo and J. Cutler, “Analytic Model and Simulation Toolkit for Space Network Communication Capacity Assessment”, Under
review in Journal of Aerospace Computing, Information, and Communication.
6. S. Spangelo, J. Cutler, A. Cohn, and K. Gilson, “Optimization-Based Scheduling for the Single-Satellite, Multi-Ground Station
Communication Problem ”, in Preparation for Operations Research.
7. S. Spangelo, J. Arlas, J. Cutler, “On-Orbit Results of the GPS Subsystem for the Radio Aurora Explorer”, in Preparation for Acta
Astronautica.
Selected Conference Proceedings:
1. S. Spangelo, D. Kaslow, C. Delp, B. Cole, L. Anderson, and J. Cutler, “Model Based Systems Engineering (MBSE) Applied to Radio
List of Publications
1. S. Spangelo, D. Kaslow, C. Delp, B. Cole, L. Anderson, and J. Cutler, “Model Based Systems Engineering (MBSE) Applied to Radio
Aurora Explorer (RAX) CubeSat Mission Operational Scenarios”, Accepted for IEEE Aerospace Conference, 2013, Big Sky, MT.
2. S. Spangelo, D. Kaslow, C. Delp, B. Cole, L. Anderson, E. Fosse, L. Hartman, B. Gilbert, and J. Cutler, “Applying Model Based Systems
Engineering (MBSE) to a Standard CubeSat”, IEEE Aerospace Conference, 2012, Big Sky, MT, March 2012.
3. S. Spangelo, J. Cutler, and D. Boone,“Assessing the Capacity of a Federated Ground Station Network”, IEEE Aerospace Conference,
2010, Big Sky, MT, March 2010.
4. S. Spangelo and J. Cutler, “Optimization of Single-Satellite Operational Schedules Towards Enhanced Communication Capacity”,
GNC Conference, Minneapolis, MN, August 2012, (Best GNC Student Paper Award).
5. S. Spangelo and J. Cutler, “Optimal Operational Planning for Interplanetary Small Satellite Exploration Missions Applied to a
Phobos Lander Mission”, iCubeSat Workshop, Boston, MA, May 2012.
6. S. Spangelo and J. Cutler, “Integrated Approach to Optimizing Spacecraft Vehicles and Operations”, International Astronautical
Congress, Cape Town, South Africa, October 2011.
7. J. Cutler, J. Springmann, S. Spangelo, and H. Bahcivan, “Initial Flight Assessent of the Radio Aurora Explorer”, Small Satellite
Conference, 2011, Logan, UT, August 2011.
8. S. Spangelo and J. Cutler, “Small satellite operations model to assess data and energy flows”, AIAA/AAS Astrodynamics Specialist
Conference, 2010, Toronto, Canada, August 2010.
9. S. Spangelo, A. Klesh, and J. Cutler, “Position and Time System for the RAX Small Satellite Mission”, AIAA/AAS Astrodynamics
Specialist Conference, 2010, Toronto, Canada, August 2010.
10. S. Spangelo, E. Gilbert, A. Klesh, A. Girard, and P. Kabamba, “Solar-Powered Aircraft: Energy-Optimal Path Planning And Perpetual
Endurance”, AIAA Guidance, Navigation, and Control Conference, 2009, Chicago, IL, August 2009.
Questions?
GPS Satellite
SoLong UAV
1U: SKCSat
3U: UKube-1