modeling and optimization of space networks to improve

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


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


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


10

Optimization

Algorithms

Applications

Conclusion

Future Work

Ground station perspective, assuming orbiting spacecraft is available

Constant communication possible


Motivation

Contributions

Modeling

Assessment

Optimization


11

Optimization

Algorithms

Applications

Conclusion

Future Work


Additive Constraints: Orbit and station locations, Minimum elevation


Motivation

Contributions

Modeling

Assessment

Optimization


12

Optimization

Algorithms

Applications

Conclusion

Future Work


Additive Constraints: Ground station availability


Motivation

Contributions

Modeling

Assessment

Optimization


13


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



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



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


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


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


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


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


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


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)


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


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


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


SMSP SOSP


32

Optimization

Algorithms

Applications

Conclusion

Future Work



Missions that exceed requirements

Motivation

Contributions

Modeling

Assessment

Optimization


SMSP SOSP


32

Optimization

Algorithms

Applications

Conclusion

Future Work



Missions that don’t satisfy requirements

Motivation

Contributions

Modeling

Assessment

Optimization


SMSP SOSP


32

Optimization

Algorithms

Applications

Conclusion

Future Work



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


File parts0 10050

36

Optimization

Algorithms

Applications

Conclusion

Future Work

Motivation

Contributions

Modeling

Assessment

Optimization

Step 2: Identify download opportunities (times above minimum elevations)


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


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


39

Optimization

Algorithms

Applications

Conclusion

Future Work

Motivation

Contributions

Modeling

Assessment

Optimization

Step 6: Compute efficiency statistics (GS, elevation, time, etc.)


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.

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