Optimizing Infra-Red Sensor Performance for Multiple Unmanned Aircraft

US Army Corps of EngineersBUILDING STRONG®

Optimizing Infra-Red Sensor Performance for Multiple Unmanned Aircraft

WIDA ConferenceReno, NV13 - 15 March 2012

Susan Frankenstein, ERDC-CRRELDaniel Stouch, Charles River Analytics Inc.Kirk McGraw, ERDC-CERL

BUILDING STRONG®Impetus FASST IRSP AMN SPARTEN

1. Impetus• Why I do Land Surface Modeling

2. Land Surface Model• FASST: Fast All-season Soil STrength

3. InfraRed Sensor Performance• IRSP

4. Airspace Model• Air Maneuver Nets

5. Unmanned Aircraft System Routing• SPARTEN: Spatially Produced Airspace Routes

from Tactically Evolved Networks

Outline

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Our goal is to truly integrate the dynamic effects of terrain and weather into Mission Command decisions.

1600 L

2200 L

1500 L

1800 L

Impetus

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Outline



Snow/IceMelt

Layer 1

Layer 2

Layer N

Infiltration

Suction

Run-offEvap/Cond

Run-off

Root uptake

Ponding

GravityFlow

Convection

Solar

Precipitation

Infrared, IR

Emitted IR

Conduction

LatentHeat

SensibleHeat

Change ofState

Deep Earth

High (canopy) vegetation

model

Low vegetation

model

FASST Water & Energy Balance

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• 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)

FASST Inputs/Outputs

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Impetus FASST IRSP AMN SPARTEN 7/18

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Outline


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Infrared Sensor Performance (IRSP) Region Specific Probability of detection

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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

max

min

( ) ( , ) ( )B T

T

T E T B occ BT

IRSP T Pd PT T T

Temp

0

0.2

0.4

0.6

0.8

1

1.2

28 29 30 31 32

Pro

babi

lity

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Probability of Detection

Probability of Occurrence

Impetus FASST IRSP AMN SPARTEN

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Outline



Infrared Sensor Performance tied to Airspace Model

The Air Maneuver Network (AMN) contains spatial, temporal and scenario cases sufficient to find IR sensor performance impacts along an entire UAS flight path.

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Step 3Repeat for each time step

Pd values calculated for each terrain element in

view-shed

1.0

0.0

IR P

roba

bilit

y of

D

etec

tion

Time

1500L

1800L

2100L

50 km

35 k

m

Step 1Create scenario dependent sensor performance maps

Step 2Find Probability of Detection for each edge (bi-directional)


Air Maneuver Network - IRSP Time Series

12112007 200012112007 210012112007 220012112007 230012122007 000012122007 010012122007 020012122007 030012122007 040012122007 050012122007 060012122007 070012122007 080012122007 090012122007 100012122007 1100

PYTHON

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Outline



SPARTEN’s Goal

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Produce flyable UAS routesthat satisfy specific constraints

to optimize ISR performanceof multiple aircraft

Raven Shadow Gray Eagle


Technical Architecture

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Cost Factor Maps

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Restricted Operating Zones

WeatherEffects (T-IWEDA)

NAIEmphasis

Transmitter Range

AMNMilitaryValue

PathLinearity

Convoy Support

SensorPerformance


Air Maneuver Network

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► Background


Candidate Solution

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Questions?

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Optimizing Infra-Red Sensor Performance for Multiple Unmanned Aircraft