Satellite constellations and Distributed Spacecraft Mission (DSM) architectures offer unique benefits to Earth observation scientists and unique challenges to cost estimators. The Cost and Risk (CR) module of the Tradespace Analysis Tool for Constellations (TAT-C) being developed by NASA Goddard seeks to address some of these challenges by providing a new approach to cost modeling, which aggregates existing Cost Estimating Relationships (CER) from respected sources, cost estimating best practices, and data from existing and proposed satellite designs. Cost estimation through this tool is approached from two perspectives: parametric cost estimating relationships and analogous cost estimation techniques. The dual approach utilized within the TAT-C CR module is intended to address prevailing concerns regarding early design stage cost estimates, and offer increased transparency and fidelity by offering two preliminary perspectives on mission cost. This work outlines the existing cost model, details assumptions built into the model, and explains what measures have been taken to address the particular challenges of constellation cost estimating. The risk estimation portion of the TAT-C CR module is still in development and will be presented in future work. The cost estimate produced by the CR module is not intended to be an exact mission valuation, but rather a comparative tool to assist in the exploration of the constellation design tradespace. Previous work has noted that estimating the cost of satellite constellations is difficult given that no comprehensive model for constellation cost estimation has yet been developed, and as such, quantitative assessment of multiple spacecraft missions has many remaining areas of uncertainty. By incorporating well-established CERs with preliminary approaches to approaching these uncertainties, the CR module offers more complete approach to constellation costing than has previously been available to mission architects or Earth scientists seeking to leverage the capabilities of multiple spacecraft working in support of a common goal

de Weck, Oliver

Foreman, Veronica

Le Moigne, Jacqueline

NASA Technical Reports Server

1 
Satellite Constellation Cost Modeling:  
An Aggregate Model
Veronica Foreman, Jacqueline Le Moigne, Olivier 
de Weck
ESTF2016 – Session B1
June 14, 2016
https://ntrs.nasa.gov/search.jsp?R=20180007289 2019-08-31T18:17:31+00:00Z
2 
Presentation Outline
•  Introduction 
–  TAT-C
–  VCR Module
•  Historical Context 
•  Aggregate model formulation
–  Motivation and Justification
–  State of the Art 
–  Limitations of Existing Models 
•  Cost Module, Version 1
–  Implementation
–  Future Revisions 
–  Impact
•  Conclusions/Future Work 
3 
Tradespace Analysis Tool for Design of Distributed Missions  
PI: Dr. Jacqueline Le Moigne, NASA GSFC
(Le Moigne: TAT-C)                                                                                     NNH14ZDA001N-AIST July 11, 2014 
 
COMPETITION SENSITIVE – FOR REVIEW PURPOSES ONLY 
1 
Scientific/Technical/Management 
 
General Definition of Distributed Spacecraft Missions: 
A Distributed Spacecraft Mission (DSM) is a mission  
that involves multiple spacecraft to achieve one or more common goals. 
1. Project Summary 
Under a changing technological and economic environment, there is growing interest in 
implementing future NASA Earth Science missions as Distributed Spacecraft Missions 
(DSM). The objective of our project is to provide a framework that will facilitate the DSM 
Pre-Phase A investigations, and that will enable to optimize the design of DSM with respect 
to a-priori Science goals by conducting trades such as “utilizing a homogeneous constellation 
in which all spacecraft and instrument payload are identical vs. utilizing a heterogeneous 
constellation by splitting bands over a formation flying vs. utilizing a disaggregated 
approach or fractionated spacecraft where functional capabilities are distributed among 
different spacecraft”.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1 – Our Trade-space Analysis Tool for Constellations, TAT-C, will enable to perform Pre-Phase A 
trades such as number of satellites, number of planes, orbit altitude, etc. , to optimize the design of a mission 
aimed at responding to specific Science goals. With a graphically-rich user interface, it could be utilized by 
Science Program Managers to optimize their Science mission portfolio or by Scientists designing their 
missions as a companion tool to Observing System Simulation Experiments (OSSEs) 
Other questions investigated with our Trade-space Analysis Tool for Constellations (TAT-C) 
could be “Which type of constellations should be chosen? How many spacecraft should be 
included in the constellation? Should they all be in the same plane? If not, how many planes 
should be utilized? Which design has the best cost/risk value?” We envision the output of our 
NRC Earth 
Science 
Decadal 
Survey 
(DS) 
TAT-C 
Tradespace 
Analysis Tool for 
Constellations 
 
Pre-Phase A 
Design 
Science 
Program 
Managers 
Earth 
Scientists 
 
Rec
om
me
nde
d 
Mis
sion
s 
Proposed 
Missions Sci
enc
e G
oal
s 
Recommended 
Missions 
Optimize Science 
Mission Portfolio 
based on Science 
Goals and DS as a 
function of Cost and 
Risk 
Observing 
System 
Simulation 
Experiments 
(OSSEs) 
New Missions Design 
OPTIONS 
(1) 
(2) 
(3) 
•  Characteris*cs+
•  Cost,+Risk,+etc.+
•  Characteris*cs+
•  Cost,+Risk,+etc.+
•  Characteris*cs+
•  Cost,+Risk,+etc.+
Average Revisit Time depends on Number of 
Planes and Number of Satellites per Plane  
Average Revisit Time depends on Orbit 
Altitude and Instrument Elevation 
Objectives: 
•  Provide a framework to perform pre-
Phase A mission analysis of Distributed 
Spacecraft Missions (DSM)
–  Handle multiple spacecraft sharing mission 
objectives
–  Include sets of smallsats up through 
flagships
–  Explore tradespace of variables for pre-
defined science, cost and risk goals, and
metrics
–  Optimize cost and performance across 
multiple instruments and platforms vs. one 
at a time
•  Create an open access toolset which 
handles specific science objectives and 
architectures
–  Increase the variability of orbit 
characteristics, constellation 
configurations, and architecture types
–  Remove STK licensing restrictions
4 
Value, Cost, and Risk Module
•  Addresses the TAT-C Objectives that require cost and risk 
evaluations; given a satellite constellation architecture, the VCR 
module will provide estimates of: 
–  Value, expressed in dollars or utility 
–  Cost, life cycle cost (RDT&E, manufacturing, launch, operations) 
–  Risk, profile of the system technical and cost risk 
•  VCR Module will enable trades between performance and value/
cost/risk more readily
This presentation addresses the need for an automated, 
integrated cost model for constellation mission design and 
the associated cost estimating challenges.
5 
Historical Context
Satellite 
Constellations
Cost 
Estimation
Image Credits from left to right [6, 5] 
6 
Building an Aggregate Cost Model 
•  Motivation 
–  Recent ‘design to cost’ and ‘cost as an independent variable’ efforts 
have changed the nature of cost estimating
–  Existing models often focus on single spacecraft or fixed architectures
•  Can be difficult to incorporate this information into the decision 
making process
•  Objective
–  Develop an automated cost estimating approach that leverages 
existing and trusted techniques and applies them to Distributed 
Spacecraft Mission (DSM) architectures
–  Build the approach in such a way that it is easily manipulated and 
highly transparent 
•  Challenges
–  Automated cost estimation often results in skepticism
–  Constellation architectures require that traditional cost estimation 
assumptions be challenged 
–  Model must be able to adapt to technological innovation
7 
State-of-the-Art and Existing Literature
•  Many widely accepted cost estimating tools exist, including: 
–  Unmanned Space Vehicle Cost Model (USCM), Version 10
–  Small Satellite Cost Model (SSCM), 2014 Release
–  NASA Instrument Cost Model (NICM), Version 7
–  QuickCost, Version 6.0
–  NASA Instrument Cost Model (NICM), Version 7
•  Popular references:
–  NASA Cost Estimating Handbook, Version 4.0
–  Space Mission Analysis and Design, 3rd Edition
•  Previous work has highlighted the limitations of these tools 
for constellation missions: 
–  Limitations of traditional cost models for high performance small 
satellites, motivating the SSCM [Abramson and Bearden, 1993]
–  Small satellite learning curve parameters, COTS components, 
technological complexity as they pertain to DSMs [Nag et al.,2014]
8 
Selected Approach
•  Interoperable, parametric cost estimating tool to interface 
with TAT-C
–  Parametric estimating allows for a top down approach
•  More appropriate in early stages of design; does not require 
extensive design decisions
•  Cost Estimating Relationships (CERs) can be easily updated 
–  Allows for relative trades between cost and capability
•  Early stage mission cost estimates are relative, not absolute, 
trade study tools
–  Outputs as .json files that mimic a traditional Work Breakdown 
Structure (WBS)
•  Plan to supplement the parametric approach with an analogous 
cost estimate to ensure model fidelity
9 
Model Comparison
USCM (Version 7) and SSCM (1998) results for TAT-C generated 
spacecraft
–  Spacecraft are identical, with IR Sensor payloads, except for total mass 
–  Payload cost differs substantially between the two models
•  Motivation for alternate payload costing approach
10 
Implementation
•  VCR Module Cost Routine combines existing models and 
applies them to DSMs
1.  Assesses mission characteristics (e.g. number of spacecraft)
2.  Costs spacecraft and payloads appropriately
•  USCM for spacecraft >= 1000kg
•  SSCM for spacecraft < 1000kg
•  NICM for primary payload instruments
3.  Leverages existing best practices to adjust for system level 
cost considerations (e.g. learning curve, design heritage)
4.  Uses current launch vehicle market prices to estimate launch 
cost and operational support requirements 
5.  Formats cost estimate and records caveats to valuation
•  Shao et al. (2014) took a similar approach to Performance-
Based Cost Modeling, leveraging USCM, SSCM, NICM
11 
Sample Output
{
    "constellationCost": {
        "totalCost": {
            "estimate": 285896.029,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "Constellation is homogeneous. Launch Vehicle was not designated, 
launch vehicle cost is set to 0. "
        },
        "rdteCost": {
            "estimate": 81346.16106,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "CER choice: Input (spacecraft total mass) to thermal RDT&E CER 2 
for spacecraft 1 is out of acceptable CER range. CER 1 was used instead. "
        },
        "drivers": "Spacecraft 1, Payload. Spacecraft 1, Operations. Spacecraft 2 IA&T. ",
        "spacecraftRank": [1,2]
    }
}
Truncated output .json
Advantages: 
-  Human readable, promotes transparency
-  Interoperable
-  In full form, follows WBS format 
12 
Sample Output
{
    "constellationCost": {
        "totalCost": {
            "estimate": 285896.029,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "Constellation is homogeneous. Launch Vehicle was not designated, 
launch vehicle cost is set to 0. "
        },
        "rdteCost": {
            "estimate": 81346.16106,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "CER choice: Input (spacecraft total mass) to thermal RDT&E CER 2 
for spacecraft 1 is out of acceptable CER range. CER 1 was used instead. "
        },
        "drivers": "Spacecraft 1, Payload. Spacecraft 1, Operations. Spacecraft 2 IA&T. ",
        "spacecraftRank": [1,2]
    }
}
13 
Sample Output
{
    "constellationCost": {
        "totalCost": {
            "estimate": 285896.029,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "Constellation is homogeneous. Launch Vehicle was not designated, 
launch vehicle cost is set to 0. "
        },
        "rdteCost": {
            "estimate": 81346.16106,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "CER choice: Input (spacecraft total mass) to thermal RDT&E CER 2 
for spacecraft 1 is out of acceptable CER range. CER 1 was used instead. "
        },
        "drivers": "Spacecraft 1, Payload. Spacecraft 1, Operations. Spacecraft 2 IA&T. ",
        "spacecraftRank": [1,2]
    }
}
14 
Sample Output
{
    "constellationCost": {
        "totalCost": {
            "estimate": 285896.029,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "Constellation is homogeneous. Launch Vehicle was not designated, 
launch vehicle cost is set to 0. "
        },
        "rdteCost": {
            "estimate": 81346.16106,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "CER choice: Input (spacecraft total mass) to thermal RDT&E CER 2 
for spacecraft 1 is out of acceptable CER range. CER 1 was used instead. "
        },
        "drivers": "Spacecraft 1, Payload. Spacecraft 1, Operations. Spacecraft 2 IA&T. ",
        "spacecraftRank": [1,2]
    }
}
15 
Sample Output
{
    "constellationCost": {
        "totalCost": {
            "estimate": 285896.029,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "Constellation is homogeneous. Launch Vehicle was not designated, 
launch vehicle cost is set to 0. "
        },
        "rdteCost": {
            "estimate": 81346.16106,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "CER choice: Input (spacecraft total mass) to thermal RDT&E CER 2 
for spacecraft 1 is out of acceptable CER range. CER 1 was used instead. "
        },
        "drivers": "Spacecraft 1, Payload. Spacecraft 1, Operations. Spacecraft 2 IA&T. ",
        "spacecraftRank": [1,2]
    }
}
16 
Sample Output
{
    "constellationCost": {
        "totalCost": {
            "estimate": 285896.029,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "Constellation is homogeneous. Launch Vehicle was not designated, 
launch vehicle cost is set to 0. "
        },
        "rdteCost": {
            "estimate": 81346.16106,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "CER choice: Input (spacecraft total mass) to thermal RDT&E CER 2 
for spacecraft 1 is out of acceptable CER range. CER 1 was used instead. "
        },
        "drivers": "Spacecraft 1, Payload. Spacecraft 1, Operations. Spacecraft 2 IA&T",
        "spacecraftRank": [1,2]
    }
}
17 
Sample Output
{
    "constellationCost": {
        "totalCost": {
            "estimate": 285896.029,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "Constellation is homogeneous. Launch Vehicle was not designated, 
launch vehicle cost is set to 0. "
        },
        "rdteCost": {
            "estimate": 81346.16106,
            "standardError": null,
            "confidenceInterval": [lowLimit, highLimit, probability]
            "caveats": "CER choice: Input (spacecraft total mass) to thermal RDT&E CER 2 
for spacecraft 1 is out of acceptable CER range. CER 1 was used instead. "
        },
        "drivers": "Spacecraft 1, Payload. Spacecraft 1, Operations. Spacecraft 2 IA&T",
        "spacecraftRank": [1,2]
    }
}
18 
Current Status and Future Work
•  Cost method version 1 is being integrated TAT-C as a set 
of MATLAB functions 
•  Short term remaining tasks:
–  Transition model to C++
–  Cost Risk Estimation, will depend on risk methodology
–  Operations and Ground Segment
•  Operations and maintenance can be most expensive 
constellation mission element
•  How reliable are existing methods for constellations and 
what is the impact of increasing automation?
•  Long term: 
–  Continued model bench marking for reliability
–  Upgrade CERs to most recent formulae
19 
Conclusion
•  Aggregate cost model leverages existing tools and applies 
them to DSM architectures, while addressing limitations of
•  This approach allows for integration of cost with early 
tradespace exploration
–  VCR is designed for TAT-C, but the form and function will 
allow for interoperability 
–  Promote cost estimating transparency in automated 
processes
–  Relative cost estimates for architecture comparison
•  Continues to reveal limits of cost estimating techniques 
for future DSM development
20 
References and Acknowledgements
1.  Wertz, James Richard., and Wiley J. Larson. Space Mission Analysis and Design. 3rd ed. Torrance, CA: Microcosm, 
1999. Print.
2.  Bearden, David A. "Small-satellite costs." Crosslink 2.1 (2001): 32-44.
3.   “TROPICS: Mission Overview." Mission Overview. MIT Lincoln Laboratory, 2015. Web. 1 June 2016.
4.  Tieu, B., J. Kropp, and N. Lozzi. "The Unmanned Space Vehicle Cost Model-Past, Present, and Future." American 
Institute of Aeronautics and Astronautics: Space 2000 Conference and Exposition. Long Beach, California. 2000.
5.   "Landsat Missions: Imaging the Earth Since 1972." Landsat Missions Timeline. US Geological Survey, 2015. Web. 17 
Sept 2015.
6.  Nguyen, Phu, et al. "Unmanned Space Vehicle Cost Model." US Air Force Space and Missile Systems Center (SMC/
FMC) 2430 (1994): 90245-4687.
7.  Lao, N. Y., T. J. Mosher, and J. M. Neff. "Small Satellite Cost Model, Version 98 InTRO." (1999).
8.  Hamaker, Joseph, and Ronald Larson. "Quick Cost 6.0: Introduction and Overview." NASA Cost Symposium 2015. 
NASA Ames Research Center, Moffett Field, CA. NASA 2015 Cost Symposium Presentations. NASA. Web. 5 Oct. 
2015.
9.  Habib-Agahi, Hamid, Joe Mrozinski, and George Fox. "NASA instrument cost/schedule model Hamid Habib-Agahi." 
Aerospace Conference, 2011 IEEE. IEEE, 2011.
10.  NASA, NASA. "Cost Estimating Handbook." Washington, DC (2008).
11.  Abramson, R. L., and D. A. Bearden. "Cost Analysis Methodology for High-Performance Small Satellites." SPIE 
International Symposium on Aerospace and Remote Sensing, Small Satellite Technology and Applications III. 1993.
12.  Nag, Sreeja, Jacqueline LeMoigne, and Olivier de Weck. "Cost and risk analysis of small satellite constellations for 
earth observation." Aerospace Conference, 2014 IEEE. IEEE, 2014.
13.  Shao, Anthony, Elizabeth A. Koltz, and James R. Wertz. "Performance Based Cost Modeling: Quantifying the  Cost 
Reduction Potential of Small Observation Satellties." AIAA Reinventing Space Conference, AIAA-RS-2013-1003, Los 
Angeles, CA, Oct. 2013.Bearden, David A. “Small Satellite Costs.” Crosslink. Aerospace Corporation, 2000. Web. 20 
May 2016.
This material is based on work supported by the National Science Foundation Graduate Research Fellowship Program. 
Guidance and support have also been provided by the Massachusetts Institute of Technology and NASA Goddard Space Flight 
Center.
21 
Thank you for your attention!  
Any questions?
22 
Additional Slides: Key Assumptions
•  Comparative, not an exact value, estimate
–  Estimate should provide an approximation that can be used 
for tradespace analysis 
•  Comparison during concept evaluation, not as direct 
budgeting tool 
•  CERs are based in historical trends; assume that the 
trends will hold into the foreseeable future 
–  Major technological changes will impact model fidelity
–  Smallsat launchers could cause significant changes 
•  Prototype, not protoflight, hardware development process 
•  Scope creep is not considered 
•  Project is executed at the optimal pace 


Satellite Constellation Cost Modeling: An Aggregate Model

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