Title: 19 April 2004
119 April 2004
Synthetic Environment Integrated Testbed
Infrared Intelligence, Surveillance, and
Reconnaissance Thread (IR ISR)
Kevin Dennen Dr. Will Clayton ERC,
Incorporated Tim Clardy Redstone Technical Test
Center
2OverView
- Purpose
- Operational Overview
- Progression
- Models Terrain and Targets
- Synthetic Imagery
- Future Planning
- Teaming Efforts With NVL
- DTE 4 Planning Details
- Summary
3SEIT IR ISR THREAD
- PURPOSE
- Operational Representation Of IR Sensor Fields Of
View (Platform Perspective) - Detect, Recognize, Identify And Locate Red
Threats On Simulated Battlefield - Provide Information On Red Forces To Populate
SPOT Reports - Test IR Sensor Performance Utilizing Test
Instrumentation - Sensor To Shooter Interface
- Conduct Battlefield Damage Assessment (BDA) Of
Enemy Forces
4SEIT IR ISROPERATIONAL OVERVIEW
Red Forces
Operational Scenario Generation
Information
Information
OTB
Distributed Test Control Center (DTCC)
Blue Forces
IR Sensor Scene Generation
EPG STORM / RPWS SPOT Reports
B3
B4
HPC
B2
B5
DEGA
HPC
HPC
Sensor
IR Battlefield Imagery
RPWS Battlefield Map
5SEIT IR ISR CREW STATIONS
IR Sensor output
Terminal to HPC computer creating IR Scene
IR sensor detecting IR energy from scene
projection test hardware
Joystick used to pan battlefield, locate red
targets, and initiate laser range finder
STORM RPWS station for SPOT report generation
6SEIT IR ISR PROGRESSION
Capability
Increase in Synthetic Representation
Capabilities In IOCs are Cumulative and Building
Upon One Another
Time
7SEIT Terrain And Targets
- Terrain was created by Yuma Proving Grounds
Digital Terrain Group - Targets came from various sources
- Signature Research Predictive Models
- RTTC Coreset Empirical Models
- Other Test Centers Visible Models Converted to
Infrared
8YPG Synthetic Infrared Terrain
9Infrared Vehicle Examples
M113 Model from Signature Research Inc.
BMP2 Model From RTTC Coreset Targets
Both Targets are Measured/Modeled for YPG Summer
Day
10IR Sensor Capabilites
- Pan Battlefied Using BG Flybox
- Ability To Turn Sensor Effects On/Off
- WFOV/NFOV
- DIS Entity Information
- Simulated Range Finder
- Automated Spot Reports of Enemy Vehicles Sent to
RPWS
11Scenes From SEIT
12FCS LSI SDD IPP 1PLATFORMS SENSORCAPABILITIES
- Manned Ground Vehicles
- Infantry Carrier Vehicle (ICV)
- Mounted Combat System (MCS)
- NLOS-Cannon (NLOS-C)
- NLOS Mortar (NLOS-M)
- Recon Surveillance Vehicle (RSV)
- Command Control Vehicle (C2V)
- Medical Vehicle Extraction (MV-E)
- Unmanned Vehicles
- Multifunction Utility Logistics
- Equipment Vehicle Transport
- (MULE-T)
- MULE Assault (Light) (ARV-A (L))
- MULE Counter Mine (MULE-CM)
- Small Unmanned Ground Vehicle
- (SUGV)
- UAV Class I IV
- Sensor Capabilities (Multiple Systems)
- Detect Targets With IR Sensor
- Conduct BDA in EO Stare Mode
- Conduct BDA with Short Range EO
- Sensor
- Conduct BDA with Medium Range EO
- Sensor
- Unattended Munitions Sensors
- NLOS Launch System (NLOS-LS)
- Intelligent Munitions System (IMS)
- Unattended Ground Sensors (UGS)
13Future Efforts
- Exploring Possible Teaming Effort With NVESD
- Increase Fidelity For Synthetic Environment
Representation - Additional SEIT Viewing portal in Washington, DC
- NVESD has developed a simulation toolset to
support the development, integration, test, and
evaluation of FCS networked sensors (UGS, UAVs,
UGVs) and Intelligent Munition Systems (IMS)
that provides - Simulation of Acoustic/Seismic Unattended Ground
Sensors with and without EO/IR Imagers - Simulation of Intelligent Munition Systems (IMS),
Mines and Countermine Equipment - Simulation of EO/IR Sensors on UAVs, UGVs, and
RSV platforms - Operator interfaces allowing crew interaction
14FCS 2-MAN RECONNAISSANCE AND SURVEILLANCE
VEHICLE (RSV) SIMULATOR
Driver DVO and IR views (Long Wave Uncooled)
Universal Controller UGS/IMS, UAV, UGV Control
Station
Scout IR views (2nd Gen FLIR, WAS, AiTD)
Driver station
Joy Stick controls
Hardware shown is based on UAMBLs Advanced
Concept Research Tool (ACRT)
SC4/MC2 SA display
15NVL COMPREHENSIVE MINE AND SENSOR SERVER (CMS2)
- Discrete, high res simulator for mines,
unattended ground sensors and Intelligent
munition systems - Operates in conjunction with force-on-force
simulation engines (OneSAF Testbed ) - Allows large scale simulation of mines or
distributed sensors with minimal network burden - Physics-based sensor models
- NVESD Acquire IR search and target acquisition
model - ARL Acoustic Battlefield Aid
- ERDC CRREL Seismic Sensor Performance Evaluation
Model
Acoustic Pd footprint from ABFA
16 NVL PORTAL CAPABILITY
- Viewing For Local Washington DC Personnel
- 135 X 65 Foot Projection System Tied To DREN
- Real Time Video Mixing Allows Up To 16 Different
Views Of Exercise To Be Presented At Once - Can Be Used In Classified Or Unclassified Mode
- Used For The 1st APP Showing
- Formal LSI SoSIL Facility
- Can Be Tied Into The WSMR IRCC As A Viewer
17 SEIT DTE 4 CAPABILITIES PLAN
- Reconnaissance and Surveillance Vehicle (RSV)
- Scout and driver stations
- Mast mounted TA/ISR sensor suite
- UAV, UGV, and/or UGS/IMS controllers
- Implement three RTTC, NVL, and Fort Knox Battle
Lab - Command and Control Vehicle (C2V)
- 5 Scout work stations with automated spot report
into Role Player Work Station (RPWS) - Similar to IOC3 implementation
- Live range vehicle interface (UAV, T72, TBD)
- Map RTTC TA3 to area in Caspian Sea database
- NVL Portal viewing for local Washington DC
attendance
18RTTC DTE4 C2V STATION ENHANCEMENTS
- Technology Transition To PC-based Scene
Generator - Overcomes 6 Platform Limit Due To Scarce HPC
Resources - Increases Portability Of Scene Generators
- Reduced Operational Cost
- Allows Operators The Ability To Quickly Switch
Between - Visible And IR Views
- Increased Fidelity And Performance Of Sensor
Parameters - Due To PC GPUs
- Provide Displays For Both Driver And RSTA
Sensor - Leverage NVESD FCS Simulations Where Applicable
19SUMMARY
- SEIT IR ISR Represents The Soldiers Eyes In
The Battlefield - IR ISR Synthetic Representation Is Advancing At
Every Opportunity - Teaming Arrangements with External Organizations,
such as NVL, and Leveraging Of Capabilities Are
Being Actively Pursued - Integration with Live Assets And Test Ranges Are
Increasing