IR Performance Surfaces

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Eustacia Griffith
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1 IR Performance Surfaces WIDA 14 March 2012 Mr. Christian Borden-AER Dr. Guy Seeley-AER Mr. Steven Lowe-AER Dr. Andreas Goroch-NRL 1

2 Outline Typical IR Sensor Performance Products Performance Surfaces Current Programs: EASEE, BTRA Mission Parameter Abstraction Implementation Conclusion 2

3 Conventional IR Sensor Performance Products Grid View Angle Time Series 3

4 Performance Surface Concepts Decision Aid Products Decision Products Operator decisions. Performance Surface Representation Performances surfaces, detection ranges, time series, system impacts, air maneuverability networks, astronomical data products. Modeled Environmental and Anthropogenic Systems Data and Intelligence Terrain NWP forecast, target signature, terrain signature, terrain mobility, clutter, social/behavioral expectations, sensor characteristics, atmospherics, astronomy. Weather obs, target specification, terrain specification, sensor specification, mission scenario, adversary operations. 4

5 Conventional Products Decision Aid Products Operator decisions. Performance Surface Representation Unused / Segregated Performances surfaces, detection ranges, time series, system impacts, air maneuverability networks, astronomical data products. Modeled Environmental and Anthropogenic Systems Data and Intelligence Terrain NWP forecast, target signature, background signature, terrain mobility, clutter, social/behavioral expectations, sensor characteristics, atmospherics, astronomy. Weather obs, target specification, background specification, sensor specification, mission scenario, adversary operations. 5

6 Conventional vs. Perf. Surface Similarities: Input: weather, background, targets, sensors. Models: MuSES, FASST, MODTRAN, Johnson Criteria, TTP. Differences: More geospatial context. Models are run over a performance surface parameter envelope [time, weather, terrain char., sensors, targets, mission parameters] Mission parameter abstraction / aggregation. Inclusion of external model influence. 6

7 IR Model Decomposition Sensor Performance Data Sensor Performance Model At-Aperture Target/Terrain Radiances Atmospheric Transmission Model (ATM) Target Radiance Model Background Radiance Model Target Model Library Weather Data Terrain Properties 7

8 Background Model Soil Strength Clay Weather: Precip, Clouds, Wind, Silt ERDC/CRREL Fast All season Soil STrength (FASST) Model Sand Soil Temperature 8 USCS Soil Types Terrain Aspect Terrain Slope

9 Performance Surfaces Example Products: ERDC/CRREL EASEE ERDC/CRREL BTRA EASEE EASEE BTRA 9

10 IR Performance Surface Example Scenario 10

11 IR Performance Surface ΔT Contrast Surface 11

12 IR Performance Surface Sensor Pd Contrast Surface + Atmospherics Pd Surface 12

13 IR Performance Surface Sensor Pd Contrast Surface + ATM + LOS Pd Surface 13

14 IR Performance Surface KML 14

15 Hypercube Data Representation Concept is to provide pre-computed physics-based solution to a problem in a form that allows for efficient runtime use. Define solution space relative to relevant system performance dimensions Employ full physics models for the computation of environmental effects and/or system responses to an underlying environment Error source is the distance between the customer s mission problem and the closest match in the pre-computed solution space. The Hypercube is an AER-developed, DoD-owned data format with supporting API s for creation and consumption Represents a well defined, generic structure for Performance Surfaces supporting any mission space XML description of dimensionality with binary payload of data Java and C/C++ API for easy integration 15

16 IR Physical Model Services Target Service,,,, Background Service,, Atmosphere Service,,,, Sensor Service Prob,, 16

17 DoDIS DoD Illumination Service (DoDIS) Operational at CNMOC, using JMBL V3.44 Local service code integrated into NITES-Next and TAWS TAWS Version 8.1 utilizes reach back capability. Recently upgraded to SLAC

18 Conclusion Provide IR performance data in a mission relevant form without heavy local analysis software. Provide multi-dimensional IR performance envelope. Well posed for SOA and allows for modeling capability to be maintained by SME. Hypercube representation provides capability to produce, transform, exchange and consume performance surface data. 18

19 Questions? POC: 19

20 BACKUP SLIDES 20

21 Technical Challenges Coordination of multiple physical model services. Generation of hypercubes that appropriately span operational parameter space. Communications with TDA clients. 21

22 Target Model Weather: Precip, Clouds, Wind, Thermal Signature Modelb Hemispherica l Representatio n of Target Radiance. 22

23 IR Performance Surfaces EASEE 23

24 IR Performance Surfaces Pd = 0.0 EASEE- Large Domain Pd = 0.5 Pd =

25 IR Performance Surfaces EASEE- Large Domain 25

26 IR Performance Surfaces EASEE Large Domain 26

27 IR Performance Surfaces Provides spatial context. Flexible scenario composition. Capability to abstract away mission parameters: Target orientation, terrain type, time of day, sensor position. Inclusion of additional models. 27

28 Hypercube Representation Wave Band Aspect Target View Angle Terrain Slope Engine State Soil Type 28

29 Composite Hypercube Composite hypercubes contain all subcomponents necessary for generation of performance surfaces. Contrast Surface Example : Target Hypercube: meteorology, target state, view angle. Terrain Hypercube: meteorology, material, slope, aspect. 29

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