OneSAF Assessment for Space Analysis

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OneSAF Assessment for Space Analysis
Richard Gombos Cole Engineering
8 Apr 08
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OneSAF Space Study Goals

• Assess OneSAF Future Space Systems on Force on
  Force Functionality
• Assess OneSAF Space Functionality
• LEO Orbits
• Satellite Representation
• Tactical Ground Station (TGS)
• Assess OneSAF Analytical Functionality

OneSAF Analytic Utility Assessment
Future Space System/TGS Military Utility Analysis

• Resulting in
• A Trained Cadre of OneSAF Analysis Users for SMDC
• Document Any OneSAF Software Issues, Creating
  OneSAF Change Requests Trouble Reports
• Identify Limitations/Constraints of OneSAF in
  Space Military Utility Analyses
• Document Results for Potential Incorporation into
  Future Space Program

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Tactical Real-Time Demonstration - Tactical Image
Support to the Warfighter

• Small Areas of Interest Limit Amount of Data
• Direct Downlink via 1Mb/s S-Band
• Preprogrammed Tasking
• Onboard Data Processing Eliminates Need for
  Ground Processor
• Correct for Instrument Response
• Some Level of Atmospheric Correction Possible
• Run ORASIS on Corrected Data Looking for
• Predetermined Spectral Signatures
• Display via Laptop Computer

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Hyperspectral Imagery (HSI)

• Reflectance from each pixel is measured at many
  narrow, contiguous wavelength intervals
• Detailed spectral signatures for every pixel
• Identify and quantify the material(s) existing
  within the pixels
• Locates and quantifies different types of
  building materials or minerals

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Detectable Items

• Camouflaged Vehicles
• Camouflaged Buildings
• OpFor units in defilade
• Nuclear, Biological and Chemical Plumes
• OpFor units under cover of foliage
• Disturbed Earth

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Methodology

• Define Common Scenarios, one with and one without
  space assets. Both include
• Camouflaged OpFor Units
• Minefields
• Concealed OpFor Units
• Define Battle Metrics
• Instrument Models and Behaviors to publish data
  necessary to calculate battle metrics
• Run both scenarios multiple times
• Perform statistical analysis on metric data to
  determine effect of space asset on battle
  outcome.

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Initial Space Assessment in OneSAF v2.0

• Represent mobility (orbital representation) of
  Future Space System
• Represent communications requirements
• Represent Tactical Ground Station (TGS)
  requirements
• Further enhance the detection capability
  (increase the fidelity) with the addition of more
  entities detectable by the HSI sensor

Hyper Spectral Sensor Implementation

• Data Collection Modifications
• Determine SLAMEM Future Space System CONOPS for
  reproduction of analysis in OneSAF
• Review the latest Space System IAP for impact on
  Assessment
• Describe current data collected by JCATS to CESI,
  and SEAS
• HSI detection of key targets (camouflaged/decoy)
• Fires
• Detection
• Target Identification
• Features Required (Turned Earth, Camo, etc)

• Future Space Entity
• Represent mobility (orbital representation) of
  Future Space Entity
• No satellite entity composer function (Two Line
  Element (TLE) must be manually specified)
• Modifies Spot satellite model
• Uses existing LEO mobility
• Inputs STK launch TLE

• HSI quarter-pixel resolution sensor effects using
  AMSAA Acquire Model
• Modified MRC/MRT sensor data to mimic an HSI
  sensor by increasing its apparent contrast value
  by a factor of 4 (in mrcMrt.xml).
• Created HSI sensor component
• Created Satellite entity with HSI and EO sensors
• Created Satellite entity with HSI and IR sensors
• Created unit with new Satellite entity
• Created enhanced detection data file which
  contains the entity types a sensor particular
  sensor type can detect
• Developed wrapper source code to read enhanced
  detection data file
• Checks if an entity is a target that can be
  detected by the sensor by comparing it to the
  data from the enhanced detection data file

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Battle Metrics

• Duration
• Casualties
• Equipment Damage
• Munition Expenditure
• Fuel Consumption
• OpFor Damage
• OpFor Casualties
• Enemy Assets Detected

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OneSAF v2.0 Data Collection

• OneSAF version 2.0 includes the following
• New Data Collector (DC) framework
• Built upon Java Annotation feature released in
  J2SE v5.0
• Java annotation is a way of adding metadata to
  Java class source code, making it available at
  run-time via reflection.
• Data is organized hierarchically for aggregation.
• Data can be organized into threads known as
  Topics
• Topics allow data associations useful for causal
  analysis
• Models, Agents and Behaviors can make any
  attribute collectable by adding metadata to the
  source code.
• Improved data collection performance (faster/more
  efficient)
• Data output can be formatted as
• CSV
• XML
• Binary

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OneSAF v2.0 Data Collection

• Enhanced Data Collection Specification Tool
  (DCST)
• Enhanced data gathering filters based upon
• A Specific Actor or Unit

The Actor tab contains the items available for
collection for every actor currently loaded in
the scenario
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OneSAF v2.0 Data Collection

• Enhanced data gathering filters based upon
• A data collection topics

The Topic tab contains all the models with
collectable information (attributes), organized
in a hierarchy of Topics
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OneSAF v2.0 Data Collection

• Enhanced data gathering filters based upon
• A Generic Entity Type

The Entity Type Tab contains the same tree as the
Topics tab, but allows the application of an
Entity Type filter to any attribute regardless of
its data type
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OneSAF v2.0 Data Collection

• Data can be collected one of two ways
• Poll - queries a topic, model, or attribute for
  its value at a user-configured rate. Polled data
  will only be collected and recorded at this rate.
• Push - pushed data is captured at the time of
  calculation

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Running OneSAF for Data Analysis

• OneSAF runs in two modes that permit statistical
  analysis of execution data
• Repeatable Mode Used to verify Model
  implementation and Modeling Infrastructure in
  OneSAF does not introduce variability into an
  exercise at runtime.
• Same SimCore
• Same Scenario
• Same Random Generator Seed
• Runs the same algorithms, through the exact same
  code paths and should result in the identical
  execution data regardless of the number of runs

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Running OneSAF for Data Analysis

• Replicable Mode Used to introduce variability
  into an exercise at runtime.
• Same SimCore
• Same Scenario
• Different Random Generator Seeds
• Runs the same algorithms, potentially through
  different code paths, resulting in a varied
  sampling of execution data across different runs.
• The Data Replication Tool and AutoPilot allow the
  user to run large numbers of executions to
  generate large sampling sizes
• Multiple runs of the exercise will show the
  variability of the scenario and the sensitivity
  of the model to different inputs.
• From this data statistical analysis can be
  performed to calculate statistical dispersion
  such as frequency distributions and standard
  deviations.

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OneSAF Space Study Assessment Schedule

• Jul 07 Dry Run of Future Space System in OneSAF
  v1.1
• Sep 07 Re Run of Future Space System in OneSAF
  v1.5
• 12 Dec Notional Future Space System
  Representation in OF v1.5.1 in SMDC Sim Center
  BLCSE Lab
• 21 Feb ISR HSI Sensors Capability
• 29 Feb OneSAF v2.0 Release
• 14 Apr Future Space System/Sensor in v2.0
• 24-29 Apr Dry run with v2.0 Space Analysis
• Apr OneSAF Study OF v2.0 Evaluation
• May Analysis Report
• Jun Possible OneSAF Space Study in Earth, Wind
  Fire (EWF) Simex
• Sep Possible OneSAF Space Study in OF

July 07 Sep 07 Dec07 Feb08 Apr 08 May
08 Jun 08 Sep 08