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Optimizing Infra-Red Sensor Performance for Multiple Unmanned Aircraft

Optimizing Infra-Red Sensor Performance for Multiple Unmanned Aircraft. Susan Frankenstein, ERDC-CRREL Daniel Stouch, Charles River Analytics Inc. Kirk McGraw, ERDC-CERL. WIDA Conference Reno, NV 13 - 15 March 2012. Outline. Impetus Why I do Land Surface Modeling Land Surface Model

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Optimizing Infra-Red Sensor Performance for Multiple Unmanned Aircraft

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  1. Optimizing Infra-Red Sensor Performance for Multiple Unmanned Aircraft Susan Frankenstein, ERDC-CRREL Daniel Stouch, Charles River Analytics Inc. Kirk McGraw, ERDC-CERL WIDA Conference Reno, NV 13 - 15 March 2012

  2. Outline • Impetus • Why I do Land Surface Modeling • Land Surface Model • FASST: Fast All-season Soil STrength • InfraRed Sensor Performance • IRSP • Airspace Model • Air Maneuver Nets • Unmanned Aircraft System Routing • SPARTEN: Spatially Produced Airspace Routes from Tactically Evolved Networks • Impetus FASST IRSP AMN SPARTEN

  3. Impetus FASST IRSP AMN SPARTEN Impetus Our goal is to truly integrate the dynamic effects of terrain and weather into Mission Command decisions. 1500 L 1600 L 1800 L 2200 L

  4. Outline • Impetus • Why I do Land Surface Modeling • Land Surface Model • FASST: Fast All-season Soil STrength • InfraRed Sensor Performance • IRSP • Airspace Model • Air Maneuver Nets • Unmanned Aircraft System Routing • SPARTEN: Spatially Produced Airspace Routes from Tactically Evolved Networks • Impetus FASST IRSP AMN SPARTEN

  5. Impetus FASST IRSP AMN SPARTEN FASST Water & Energy Balance Precipitation Infrared, IR High (canopy) vegetation model Solar Low vegetation model Sensible Heat Emitted IR Latent Heat Evap/ Cond Snow/Ice Melt Run-off Ponding Root uptake Change of State Infiltration Conduction Layer 1 Convection Suction Run-off Layer 2 Gravity Flow Layer N Deep Earth

  6. Impetus FASST IRSP AMN SPARTEN FASST Inputs/Outputs • Inputs • Meteorological Data (Dynamic) • Forecasted Weather, Observations • Soil Data (Static) • Number of Layers, Layer Thickness & Type, Material Properties • Initial Moisture & Temperature Profile; Snow Depth/Density • Vegetation Data (Static) • Type – High and Low; Density, Height • Site Specifics (Static) • Latitude, Longitude, Elevation, Slope, Aspect, Ground water level • Time offset from GMT • Outputs • Soil Temperature; Moisture, Ice and Vapor Content • Freeze/Thaw Depths; Surface State (Frozen/Thawed) • Snow Depth; Snow Density; Surface Ice Thickness • Vegetation Temperatures • Surface Energy Fluxes • Soil Strength (0-6”, 6-12” RCI, CBR); Slippery Factor (W/D/S/I)

  7. Impetus FASST IRSP AMN SPARTEN

  8. Outline • Impetus • Why I do Land Surface Modeling • Land Surface Model • FASST: Fast All-season Soil STrength • InfraRed Sensor Performance • IRSP • Airspace Model • Air Maneuver Nets • Unmanned Aircraft System Routing • SPARTEN: Spatially Produced Airspace Routes from Tactically Evolved Networks • Impetus FASST IRSP AMN SPARTEN

  9. Statistically accurate Does not require detailed knowledge of the target location and/or orientation Produces an output product for an entire region Applicable to terrain with a large variety of elevation and surface types Probability of Detection Probability of Occurrence 0.18 1.2 0.16 1 0.14 0.8 0.12 Probability 0.1 0.6 0.08 0.4 0.06 0.04 0.2 0.02 0 0 28 29 30 31 32 Temp Infrared Sensor Performance (IRSP) Region Specific Probability of detection • Impetus FASST IRSP AMN SPARTEN

  10. Outline • Impetus • Why I do Land Surface Modeling • Land Surface Model • FASST: Fast All-season Soil STrength • InfraRed Sensor Performance • IRSP • Airspace Model • Air Maneuver Nets • Unmanned Aircraft System Routing • SPARTEN: Spatially Produced Airspace Routes from Tactically Evolved Networks • Impetus FASST IRSP AMNSPARTEN

  11. Impetus FASST IRSP AMN SPARTEN 1.0 IR Probability of Detection 0.0 Infrared Sensor Performance tied to Airspace Model Step 3 Repeat for each time step The Air Maneuver Network (AMN) contains spatial, temporal and scenario cases sufficient to find IR sensor performance impacts along an entire UAS flight path. Step 2 Find Probability of Detection for each edge (bi-directional) 1500L Step 1 Create scenario dependent sensor performance maps 1800L Pd values calculated for each terrain element in view-shed 2100L Time 35 km 50 km

  12. Impetus FASST IRSP AMN SPARTEN Air Maneuver Network - IRSP Time Series 12122007 1100 12112007 2100 12112007 2200 12112007 2300 12122007 0000 12122007 0100 12122007 0200 12122007 0300 12122007 0400 12122007 0500 12122007 0600 12122007 0700 12122007 0800 12122007 0900 12122007 1000 12112007 2000 PYTHON

  13. Outline • Impetus • Why I do Land Surface Modeling • Land Surface Model • FASST: Fast All-season Soil STrength • InfraRed Sensor Performance • IRSP • Airspace Model • Air Maneuver Nets • Unmanned Aircraft System Routing • SPARTEN: Spatially Produced Airspace Routes from Tactically Evolved Networks • Impetus FASST IRSP AMN SPARTEN

  14. Impetus FASST IRSP AMN SPARTEN SPARTEN’s Goal Produce flyableUAS routes that satisfy specific constraints to optimize ISR performance of multiple aircraft Raven Shadow Gray Eagle

  15. Impetus FASST IRSP AMN SPARTEN Technical Architecture

  16. Impetus FASST IRSP AMN SPARTEN Cost Factor Maps Sensor Performance Restricted Operating Zones Transmitter Range PathLinearity Military Value AMN NAI Emphasis Weather Effects (T-IWEDA) Convoy Support

  17. Impetus FASST IRSP AMN SPARTEN Air Maneuver Network • Background

  18. Impetus FASST IRSP AMN SPARTEN Candidate Solution

  19. Questions?

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