## Virtual Mission Lifecycle (MLee, Shafto, Medina)

This section describes NASA information technology efforts that advance
the mission lifecycle process by providing evolutionary mission
information systems, comprehensive design verification and validation,
concurrent lifecycle phase engineering, and man-machine interface
optimization. The IT efforts are categorized as Virtual Mission
Lifecycle IT to reflect the heavy emphasis on software-based-modeling
and simulation. Based on the technical objective and approach, the VML
IT area is divided into five sub areas: 1) Mission system knowledge
engineering, 2) Spacecraft system performance modeling and simulation,
3) Mission system operation behavior modeling and simulation, and 4)
Rapid integration and test environment, and 5) Human-in-the loop process
modeling and training. Each sub area is described in three aspects,
capabilities pursued, technologies developed or infused, and example
products that are currently under development. Each example product is
presented with its general mission relevance and specific mission
application along with the technology readiness level range.

### Mission System Knowledge Engineering

###  Mission system knowledge engineering IT supports development of advanced information systems for representation, exploration, derivation, and distribution of the mission system information. This section introduces evolutionary mission knowledge systems that provide the bridging between domain-specific knowledge and information technology to enable multi-disciplinary knowledge exchange. 

**3.4.1.1Capabilities**

-   Requirement tracking

-   Mission system design knowledge representation

-   Mission design knowledge representation

-   Mission science knowledge representation

-   Intelligent mission information system

    2.  **Technology**

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-   Modeling Languages

-   Intelligent Agent

-   Distributed Component

-   Object-oriented Database

    2.  **Products**

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-   SPICE Ancillary Information System (Chuck Acton/JPL)

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-   *Description:* SPICE is a multi-mission spacecraft state archiving
    mechanism that supports science data analysis. This task extends the
    SPICE kernels to support future mission needs including surface
    rovers, control net, formation flying, and mission simulation and
    visualization. The extension provides multiple reference system
    integration, attitude analysis, and API data structures. Future work
    includes representation of continuous dynamics model and system
    state uncertainty.

-   *TRL*: 6-8

-   *Funding Profile('00.'01/'02):* 253/262/TBD (Code-S Info. System)

-   *POC*: Chuck Acton (chuck.acton@jpl.nasa.gov)

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-   Sax/Luthor

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-   *Description:* An object-oriented mission model code generator
    (lexical analyzer and parser generator pair) that automatically
    generates C++ programs from mission model scripts. Virtual Mission
    project at JPL employees Sax/Luthor for creating mission system
    property simulation software where a structured mission system
    property description is provided along with a syntax grammar
    specification. Future work includes dynamic code generation and
    inference engine integration for on-board autonomy support.

-   *TRL*: 3-4

-   *Funding Profile('00.'01/'02):* 150/150/TBD (Code-S Info. System)

-   *POC*: Richard Weidner (Richard.weidner@jpl.nasa.gov)

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-   Intelligent Mission Model Agent

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-   *Description:* A set of domain-intelligent mission information
    service agents that provide high-level information by applying
    domain-specific analyses to the mission data products. The available
    service agents include Target agent (science information),
    Trajectory Agent (navigation information), Telecom Agent (telecom
    resource information) and Telemetry Data Agent (telemetry
    processing). Future work plans to develop a Structure Agent (CAD
    products) that can provide relevant structural information to a wide
    range of subsystems for various structural impact analyses.

-   *TRL*: 3-4

-   *Funding Profile('00.'01/'02):* 200/200/TBD (NGI, ISE)

-   *POC*: Richard Weidner
    ([Richard.weidner@jpl.nasa.gov](mailto:Richard..weidner@jpl.nasa.gov))

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-   QUORUM

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-   *Description*: QUORUM measures the degree of contextual association
    of large numbers of word pairs in narratives or other text to
    produce models that capture the contextual structure of the text. It
    compares models to measure their degree of similarity. By ranking
    text items on their degree of similarity to a query model, QUORUM
    can retrieve the items that are most relevant to the query. These
    methods and software tools serve as the basis of new search and
    retrieval capabilities which have been validated in aviation-safety
    and other contexts requiring rapid search and response.

-   TRL=6

-   POC: Michael W. McGreevy (mmcgreevy@mail.arc.nasa.gov)

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-   Science Organizer

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-   *Description*: A centralized, web-based digital project library of
    heterogeneous scientific information, including datasets, documents,
    images, and field and lab records. ScienceOrganizer combines the
    functionality of a database, a document management system, and a
    hypermedia information space. A key feature of ScienceOrganizer is
    its use of a semantic hyperlinks to track and organize interrelated
    information resources within the repository. Cross-linkages capture
    important semantic relationships that assist users in navigating
    through the information space. These links also are useful for
    performing inference and summarization.

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-   *TRL:* 6

-   *Funding Profile('00.'01/'02) :*

-   *POC:* Richard M. Keller (rkeller@ptolemy.arc.nasa.gov)

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-   CIP(John Schreiner/ARC)

-   Shift Handover Tool (Jay Trimble/ARC)

-   Web-based Collaboration (VSDE/GSFC)

-   Smart Product Models (GSFC)

-   Science Proposal Planning Tools (GSFC)

###  Spacecraft System Performance Modeling and Simulation

Performance modeling refers to analysis methods for predicting the
performance range of a system based on its design while performance
simulation refers to software implementation of the predicted system
performance properties. For complex systems with non-linear system
response, the performance modeling requires an iterative optimization
with an imbedded simulation. This section introduces example research
products that combine modeling and simulation for performance analysis
of critical subsystems of a typical spacecraft system.

**3.4.2.1Capabilities**

-   Guidance and Control dynamics

-   Avionics Flight software (C&DH, Scheduler, etc)

-   On-board Processing

-   Instrument Systems

-   Tele Communication

-   Power

    2.  **Technology**

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-   Physics-based

-   Information flow

-   Deterministic Analysis

-   Non-deterministic Analysis

    2.  **Products**

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-   ROAM (Rover Operation Analysis Model)

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-   *Description*:Multi-body dynamics modeling and simulation for rover
    performance analysis. The system interfaces with a rover control
    software to analyze the feasibility of the required traverse
    operation interacting with terrain models. ROAM is integrated in the
    CLARITY architecture for providing rover dynamics simulation and
    interface mechanism to instrument measurement simulation software.
    The future development will extend the dynamics model to integrate
    soil mechanics.

-   *TRL:* 3-5

-   *Funding Profile('00.'01/'02) :* 200/200/200

-   *POC:* Dr. Kar-Ming.Cheung@jpl.nasa.gov

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-   COMP (Complex Optics Modeling and Prescription)

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-   *Description*: COMP estimates performance properties of a wide range
    of optical components in a complex telescope instrument based on
    acquired image products. COMP is used for model-based adaptive
    optics system design and control for NGST.

-   *TRL:* 6-7

-   *Funding Profile('00.'01/'02) :* 300/450/600

-   *POC:* Dr. Kar-Ming.Cheung@jpl.nasa.gov

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-   TFP (Karming Cheung/JPL)

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-   *Description*: TFP is an operational multi-mission
    telecommunications link analysis tool based on MATLAB. It accepts
    spacecraft, planet, and tracking station ephemerides, spacecraft
    attitude data, and link configuration data to generate predictions
    of link parameters to support project planning and analysis. Its
    batch mode counterpart UTP generates link predictions to support
    project planning and sequencing and to configure DSN telemetry
    subsystems.

-   *TRL:* 6-7

-   *Funding Profile('00.'01/'02) :* 300/450/600

-   *POC:* Dr. Kar-Ming.Cheung@jpl.nasa.gov

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-   Livingston (Nichola Muscettola /ARC)

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-   *Description:* Livingstone accepts a model of the components of a
    complex system such as a spacecraft or chemical plant and infers
    from them the overall behavior of the system. Livingstone also notes
    which commands are being given to the system and what observations
    are available. From this, Livingstone is able to monitor the
    operation of the system, diagnose its current state, determine if
    sensors are giving impossible readings, recommend actions to put the
    system into a desired state even in the face of failures and so on.

-   *TRL:* 7

-   *Funding Profile*

-   *POC:* Nicola Muscettola (<mus@ptolemy.arc.nasa.gov>)

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-   Java Pathfinder (John Schreiner / ARC)

-   Integrated Optics (GSFC)

-   Operations online collaboration (GSFC)

### 3.4.3 Mission System Operation Behavior Modeling and Simulation

Mission system operation behavior modeling and simulation technology
addresses development of realistic virtual subsystems that can be
operated in a manner similar to the operation of a real mission system.
The operation includes command sequence composition, operation
scheduling, system status reporting, science data production, and
resource profiling.

**3.4.3.1Capabilities**

-   Operation feasibility analysis

-   Operation risk analysis

-   Science-return probability analysis

-   Operation-centric performance requirement analysis (reverse design)

-   Automated science observation opportunity analysis

    2.  **Technologies**

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-   Operation behavior modeling

-   Model-based analysis (automated design space exploration)

-   Time-based simulation

-   Real-time simulation

-   2.  **Products**

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-   Ripples-MicroHelm (Richard Weidner/JPL)

    -   *Description:* A PC-based scalable mission operation console for
        comprehensive spacecraft system state visualization. The
        visualization provides intelligent interpretation of critical
        subsystems including navigation, attitude control,
        telecommunication, and instruments. Three Micro-helm systems are
        currently in use performing; science scenario validation for
        Deep Space 1, real-time telemetry visualization for Mars
        Odyssey, and virtual in-situ environment simulation for Mars
        Technology program. Future work includes integrated
        visualization of multiple spacecraft system states and
        interactive analysis of simulated vs. achieved system states.

    -   *TRL*: 4-5

    -   *Funding Profile('00.'01/'02):* 175/175/175

    -   *POC*: Dr. Richard Weidner (Richard..weidner@jpl.nasa.gov)

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-   DSENDS (Dynamics Simulator for Entry, Descent and Surface Landing)

    -   *Description*: DSENDS is a high-fidelity, multi-mission
        spacecraft simulator that models the multi-body, structural
        flexibility, environment, terrain/sensor interactions, and
        spacecraft devices during Entry, Descent and Landing (EDL) on
        planetary and small bodies. The specific use of DSENDS within
        the Mars Program is for the Smart Lander 2007 mission where it
        is augmented with high-fidelity aerodynamic subroutine libraries
        from NASA Langley. Mars-DSENDS will provide the real-time,
        end-to-end system simulation for the verification of flight
        software during the precision landing and hazard avoidance
        EDL phases of the Smart Lander mission.

    -   *TRL :* 4 -- 6

    -   *Funding Profile('00/'01/'02)*: 0/775/1085

    -   *POC*: J. (Bob) Balaram (<J.Balaram@jpl.nasa.gov>)

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-   ISHTAR

    -   *Description*: ISHTAR develops synthetic sites and virtual
        instrument prototypes so that in-situ explorations can be
        virtually executed with high-fidelity measurement simulation.
        The site properties include geomorphology, material, soil
        mechanics, and aerodynamics. The instrument properties include
        signal integration, system noise, operation, and information
        derivation. ISHTAR provides virtual site and virtual instrument
        systems for DSENDS and ROAM, and various on-board processing
        tasks. The ISHTAR team also collaborates with the IS autonomy
        test-bed team at ARC.

    -   *TRL :* 4 -- 6

    -   *Funding Profile('00/'01/'02)*: 400/420/450

    -   *POC*: Meemong Lee (meemong.lee@jpl.nasa.gov)

-   VIS (Larry Edwards/ARC)

### Rapid Prototyping & Test-bed Architecture

Modeling and Simulation technology provides a cost effective prototyping
mechanism where a system can be virtually constructed and operated.
Virtual prototypes and synthetic environments enable concurrent
engineering of the mission lifecycle phases where integration and
testing of a mission system can be performed in parallel with the
mission system design and development.

**3.4.4.1Capabilities**

-   Strategic feedback systems for mission design & engineering

-   Tactical feedback to operations team

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-   Incremental software and hardware subsystem integration and testing

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-   Plug-&-Play system integration

-   Automated test scenario generation

    2.  **Technology**

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-   Hardware-in-the-loop simulation

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-   Multi-level subsystem interface protocol

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-   Progressive system modeling and simulation

    2.  **Products**

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-   ASAT (Avionics System Analysis Testbed)

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-   *Description*: COT-based avionics system testbed for rapid
    prototyping and analysis of avionics flight software.

-   *TRL:*

-   *Funding Profile('00.'01/'02) :*

-   *POC:* Savio Chau,( savio.chau@jpl.nasa.gov)

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-   Terrain Server & Rover FSW Analysis

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-   *Description*: Synthetic digital elevation model generation and
    distribution utilizing massively parallel computation platform. The
    system is applied to analyze the performance of autonomous rover
    navigation flight software by executing the software in parallel
    over a synthetic terrain. The synthetic terrain can be also accessed
    via TCP/IP socket interface for other applications.

-   *TRL:* 5

-   *Funding Profile('00.'01/'02) :*

-   *POC:* Dave Curkendall( dave.curkendall@jpl.nasa.gov)

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-   Simulated Science Scenario

    -   *Description:* This task provides an end-to-end science
        processing test-bed by integrating the mission data products
        acquired from the simulated environment with a real ground
        operation environment at JPL which includes downlink,
        calibration, analysis, and derived data product generation. The
        science scenario testbed is currently applied to validate the
        in-situ science exploration scenario of the MER project in
        conjunction with WITS and VIS.

    -   *TRL*: 5-7

    -   *Funding Profile('00.'01/'02):* 600/600/600

    -   *POC: Dr. Eric De Jong*(<eric.m.dejong@jpl.nasa.gov>)

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-   Programmable Virtual Mission (Meemong Lee/JPL)

    -   *Description:* PVM focuses on composing an autonomously
        explore-able mission model space whose parameters can be mapped
        with the science requirement analysis. The coupling of science
        requirement specification and the modeling enables concurrent
        engineering of mission system design and observation scenario
        design processes. PVM supported MICAS (Miniature Imaging Camera
        And Spectrometer) observation planning for Deep Space 1 mission.
        Future work includes integration of the simulation capability
        for scenario verification.

    -   *TRL*: 3-5

    -   *Funding Profile('00.'01/'02):* 250/250/TBD (Code-S Info.
        System)

    -   *POC*: Dr. Meemong Lee(meemong.lee@jpl.nasa.gov)

### Human-based Process Modeling and Training

Modeling and Simulation of human behavior and cognitive process
introduces the human-centric perspectives to the mission lifecycle
process for enhancing communication, coordination, and collaboration
between multi-disciplinary teams as well as between humans and machines.

### Capabilities

-   Collaborative design environment

-   Ground operation cost/risk analysis

-   Work-flow analysis

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-   Human-centric data/information representation

-   Distributed control

    2.  **Technology**

-   Human operator modeling

-   Work-flow modeling

-   Visualization

-   Sonification

    2.  **Products**

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-   WITS (Web Interface for Telescience)

    -   *Description:* WITS is an Internet-based tool that enables
        scientists to fully participate in Mars lander and rover mission
        operations from their home institutions. WITS provides downlink
        data visualization and uplink plan generation. Distributed users
        can collaborate in visualizing downlink data, target selection,
        and plan generation. Simulation is provided to view predicted
        plan execution.

    -   TRL: 6

    -   *Funding Profile('00.'01/'02): 125/200/200*

    -   *POC: Dr. Paul Backus* (<paul.g.backus@jpl.nasa.gov>)

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-   OFAN (Asaf Degani/ARC)

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-   *Description:* OFAN is a formal, mathematically-based, approach to
    the analysis of operator interaction with machines. A formal
    methodology for verification of interface correctness is used.
    Additionally, a formal procedure for display synthesis, whose
    objective is to provide a succinct and correct interface for the
    specified task, is briefly discussed. Special attention is placed on
    the analysis of pilots\' interaction with automated flight control
    systems onboard a modern commercial aircraft. Since the approaches
    used in OFAN are highly formal, current research focuses on how they
    can be integrated into standard engineering packages such as MATLIB.

-   *TRL: 5*

-   *Funding Profile*

-   *POC:* Asaf Degani (adegani@mail.arc.nasa.gov)

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-   Brahms (Bill Clancey/ARC)

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-   *Description:* Brahms is a multiagent simulation tool for modeling
    the activities of teams, objects, documents, and computer systems. A
    Brahms model reveals how work actually gets done, especially how
    people interact with each other and with advanced technology. As a
    result, Brahms models can help mission-system designers to
    understand how tasks and information actually flow between people
    and machines, what work is required to synchronize individual
    contributions, and how tools hinder or help this process. Workflow
    diagrams generated by Brahms are the emergent product of local
    interactions between agents and systems, not pre-ordained,
    end-to-end paths. Applications of Brahms include system requirements
    analysis, instruction, design of software agents, and a workbench
    for analyzing and improving procedures, practices, and tools.

-   *TRL: 5*

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-   Apex (Roger Remington/ARC)

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-   *Description*: APEX predicts human performance and human error using
    two component technologies. First, the model incorporates certain
    decision-making biases. Habit-capture errors are generally
    efficient, but prescribe incorrect behavior in some situations,
    leading to errors which are predictable (and therefore possibly
    avoidable). Second, the model incorporates a number of mechanisms
    that suppress reliance on fallible heuristics. For example, mental
    rehearsal is often used to retain critical information. Anything
    that prevents or interferes with mental rehearsal can therefore be a
    potential cause of error. We have demonstrated how this approach can
    be used to predict operational errors in the domains like spacecraft
    ground control.

-   *TRL: 3*

-   *Funding Profile*

-   *POC:* Roger Remington (rremington@mail.arc.nasa.gov)

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-   CATS (Todd Callantine /ARC)

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-   *Description:* The Crew Activity Tracking System (CATS) predicts and
    interprets operator activities within a human-centered supervisory
    control framework. CATS is designed to predict activities based on
    anticipated mode usage; however, when interpreting operator actions,
    it can revise its expectations if the operator chooses an
    alternative, but valid, mode. This approach allows CATS to
    distinguish operator prerogative from human error. CATS has been
    validated in studies of human operators and controllers interacting
    with complex control systems and automated planning-scheduling
    systems.

-   *TRL : 5*

-   Funding Profile

-   POC: Todd Callantine (tcallantine@mail.arc.nasa.gov)

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-   Hierarchical Mission-Plan Decomposition (Jay Trimble/ARC)

-   Super Resolution Display (Peter Cheesman/ARC)

-   ShowTime 2.0 (Beau Watson/ARC)