OneSAF Assessment for Space Analysis

1. OneSAF Assessment for Space Analysis Richard Gombos Cole Engineering 8 Apr 08

2. 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 Space Study Goals 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

3. 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

4. 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

5. Camouflaged Vehicles Camouflaged Buildings OpFor units in defilade Nuclear, Biological and Chemical Plumes OpFor units under cover of foliage Disturbed Earth Detectable Items

6. 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. Methodology

7. 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 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

8. Duration Casualties Equipment Damage Munition Expenditure Fuel Consumption OpFor Damage OpFor Casualties Enemy Assets Detected Battle Metrics

9. 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 OneSAF v2.0 Data Collection

10. Enhanced Data Collection Specification Tool (DCST) Enhanced data gathering filters based upon: A Specific Actor or Unit OneSAF v2.0 Data Collection The Actor tab contains the items available for collection for every actor currently loaded in the scenario

11. Enhanced data gathering filters based upon: A data collection topics OneSAF v2.0 Data Collection The Topic tab contains all the models with collectable information (attributes), organized in a hierarchy of Topics

12. Enhanced data gathering filters based upon: A Generic Entity Type OneSAF v2.0 Data Collection 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

13. 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 OneSAF v2.0 Data Collection

14. 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 Running OneSAF for Data Analysis

15. 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. Running OneSAF for Data Analysis

16. 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