Colorado Springs Co718-683-8733

11/23/14

Space Radar & FCS-BCTSpace Radar & FCS-BCTSystem Effectiveness System Effectiveness

AnalysisAnalysisSMDC StudySMDC Study

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Agenda

• Study Objectives Overview– Review of Study Issues, MOE, and Analysis Products

• FCS-BCT Scenario Overview• Assumptions Update• SEAS Force Composition• Space Radar Composition

• ISR Collection Scheduler

• Run Matrix Summary and Changes

• Overview of Results (all study cases)• Conclusion & Recommendations for Further Study• Detailed Analysis (Base Case – 100 Runs)

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Study Issues

• Study Issue 1. What are the impacts on BCT ground effectiveness with varying priorities of Army FCS BCT information requests?

– Objective 1. [Effectiveness] Can the BCT meet mission vignette objectives, given varying priorities of information requests?

– Objective 2. [Efficiency]. How long does it take the BCT to achieve mission vignette objectives given varying priorities of information requests?

– Objective 3. [Lethality] What is the loss exchange ratio of the BCT to Threat while achieving the mission vignette objective, given varying priorities of information requests?

– Objective 4. [Survivability] How many BCT systems are lost achieving the mission vignette objectives, given varying priorities of information requests?

• Study Issue 2. What are the impacts on BCT ground effectiveness with varying schedule algorithms? (with similar objective.) Varying inputs?

• Study Issue 3. What are the impacts on BCT ground effectiveness with varying ISR collection agents/platforms? (with similar objective.)

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Measure of Effectiveness (MOE)

• (1) Mission accomplishment. Does the BCT achieve the minimum requirements for mission accomplishment defined by the mission vignettes?

• (2) Time to Complete Mission. What is the time required for the BCT to achieve the minimum requirements for mission accomplishment defined by the mission vignettes?

• (3) Loss Exchange Ratio (LOE). What is ratio of Blue to Threat system losses incurred while the BCT achieves the minimum requirements for mission accomplishment defined by the mission vignettes?

• (4) System Loss. How many BCT platforms are lost while the BCT achieves the minimum requirements for mission accomplishment within the vignettes?

• (5) Detection History. What is the per minute record of sensor-target detections while BCT achieves the minimum requirements for mission accomplishment defined by the mission vignettes?

• Make sure scenario, blue TTPs, and threat TTPs provide an opportunity to measure OPTEMPO.

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Required Analysis Products

Optimization

• Developing a simulated scheduler that– takes pre-planned inputs based on global deck for all ISR

systems– schedules information requests based on constraints from SR

constellation capabilities– evaluates Army FCS information requests for collection.

• Develop a BCT and below maneuver vignette to support analysis using information requests as part of the global collection plan.

• Show impacts of System Response in terms of ground maneuver measures of effectiveness (MOE).

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Study Assumptions - Update

• FCS-BCT with organic UAV & UGS Sensors provide continuous coverage (unrealistic)

• Red Force has comparable force capabilities, including satellite access

• Communication time delays are constant (unrealistic)

• UAVs are un-killable (unrealistic)• SR is the only global ISR collection asset

(unrealistic)

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SEAS FCS-BCT & SR Scenario

Colorado Springs Co718-683-8733

11/23/14

SEAS Overview

System of Systems

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Satellites ● UAVs ● GSR ● Attack Help ● Dismount Units ● Mounted Units ● TBMs

Where SEAS “Fits In”

SEAS represents an important tool for military utility analysis with emphasis on space based ISR and communication systems that provides unique capability to conduct trade studies and “what if” analyses

Study Plan• Scenario• Data• Tool(s) Selection

Find areas or trendsthat warrant moredetailed exploration

SEASLarge Trade Space

Other ModelsExtended Air Defense Simulation (EADSIM)

Vector-in-Commander (VIC)

JANUS

Satellite Tool Kit (STK)

Extended Air Defense Testbed (EADTB)

Simulation Location & Attack of Mobile Enemy Missiles (SLAMEM)

Joint Conflict and Tactical Simulation (JCATS)

Analyze SEAS Results

Few Runs

“Tends to Cause and Effect”

Answer

“Cause and Effect By This Much” Answer

Study Issue

Analyze ResultsStudy Complete

Numerous Runs

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SEAS Overview

● SEAS is a study-driven, agent-based, military utility analysis tool

● Physics-based, stochastic, Monte Carlo simulation

● Initially developed to support the military space acquisitions community

● Used to explore the effects of space and C4ISR system performance characteristics and concept of operations upon combat outcomes

● Part of the Air Force Standard Analysis Toolkit (AFSAT)

● Part of the Air Force Space Command M & S Toolkit

● 100% Government-owned software

● Runs on Windows (PC) computers

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● A core team of government, FFRDC, and SETA contractors guide the development of SEAS based on the needs of the user community

● The SEAS user community is quickly growing and includes several organizations across government and industry

0

TEAMTEAMSEASSEAS

SMC/TDGov’t SponsorModel Manager, TEAM SEAS Lead

AerospaceFFRDC

Core TEAM SEAS Member

SPARTA, Inc.SETA Contractor

SEAS Developer, Core TEAM SEAS

Member

RANDCorporationCore TEAM SEAS

Member UserCommunity

SEAS User Community

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Operations Other Than

War

Operations Other Than

War

Homeland Defense/ Security

Homeland Defense/ Security

Special OperationsSpecial

Operations

Small Scale ContingenciesSmall Scale

Contingencies

Architecture Evaluations

System Performance Analysis

CONOPS Exploration

Requirements Determination/Analysis

Wargame Analysis

Trade-Off Analysis

Force Mix/Force Structure Analysis

Applications of SEAS

Major Combat

Operations

Major Combat

Operations

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Example: SEAS Simulation of Maneuver Behavior (24 Tanks)

Multi-Agent Simulation of Complex Systems

Self Organized Behavior Emerges from Local Rules

Yes, Ants can be modeled in SEAS…

Observe

Orient

Decide

Act

If no enemy detected:• Stay in formation

• Move Towards Objective

If enemy detected:• Task Other Sensors• Engage it

When fired upon:• Take Defensive Action• Task Sensors• Return Fire or Call Fire

Support

When Operational Picture Changes

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SEAS Model Construction

Graphic illustration taken from Multi-Agent Systems, Jacques Ferber, Addison-Wesley, 1999.

• SEAS provides an N-dimensional “playground” for exploration

Slide adapted from EINSTEIN: An Artificial Life Approach to War, Andy Illachinski, CNA, 2000.

Aggregated forces(agents)

Individual combatants(agents)

FORCE

The SEAS User

UNITagent

PLATFORMagent

Agents interact with each other and their environment through user-defined sensors, weapons, communications gear (devices)

SEAS models contain hierarchies of user-defined agents

Agents contain user-defined rules (programmable logic) which define their actions and behaviors

Outcomes emerge from the complex interactions of agents

Variable Resolution

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SEAS Virtual Battlespace

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• Units can own other units (sub-units), platforms and equipment• There are four key concepts that apply to unit agent actions and

interactions:– The Local Target List (LTL)– The Local Orders List (LOL)– The Target Interaction Range (TIR)– The Broadcast Interval (BI)

Unit Agent Overview

- Commands

- Target Sightings

- Broadcast Variables

Target

Weather

Terrain

Day/Night

I’d better surrender

Unit Agent

Comm

Owned Platforms

Weapons

Sensors

LocalTarget List• TOS 1• TOS 2• etc.

User ProgrammedBehaviors

• Perception• Awareness• Knowledge• Understanding• Decisions

LocalOrders List• Formation Column• Move Location• etc.

Moving

Personnel

Owned Sub-units

BroadcastVariables

_____________________

TPL

TPL

TP

L

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The Local Target List

● Each unit maintains a Local Target List (LTL) containing all enemy platforms/units that it is aware of

● This LTL forms the unit’s sensed tactical picture of the battlefield

● Enemy plaftforms/units enter the LTL through:─ Detection by onboard sensors

─ Via communications channels

● Enemy targets not updated by one of the two above methods within the unit’s “Threat_Hold” time are removed from the LTL

● Enemy targets that are killed and BDA'd are also removed from the LTL

UAV

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The Local Orders List

● Each unit maintains a Local Orders List that contains a stack of orders for execution

● Orders enter the list from onboard programmed behavior or flow down from higher echelons via communications channels

● Locally issued orders take precedence over externally generated orders

UAV

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Target Interaction Range & Broadcast Interval

● Targets outside the unit’s interaction range are not posted on the LTL even though sensor sightings for that target are available on the communications channel

● The maximum target range is set by default to 2.5 times the maximum owned sensor or weapon range whichever is larger, however the analyst can override this default behavior using the “Max_Target_Range” parameter for units and platforms

● The agent broadcasts all targets on its LTL at an interval defined by the “Broadcast_Interval”

MaxTarget RangeA

KA

Int

era

ctio

n R

ang

e

MaxSensorRange

MaxWeaponRange

UAV

Colorado Springs Co718-683-8733

11/23/14

The FCS BCT Scenario

Caspian Sea 20 Vignette 1

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Caspian Sea Strategic Context

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FCS BCT – A Unit Task Organization

UA

HHC BIC

FSB NLOS

V

FCS (Manned):

ICV C2V R&SV MCS NLOS Mortar FCS Cannon FCS MV-Evac FCS MV-T FRMV

FCS (Unmanned): ARV-RSTA ARV A (L) ARV-Assault MULE – Transport/Retrans NLOS LS

SUGV MULE w/ GSTAMIDS

FCS (Manned):

ICV C2V R&SV MCS NLOS Mortar FCS Cannon FCS MV-Evac FCS MV-T FRMV

FCS (Unmanned): ARV-RSTA ARV A (L) ARV-Assault MULE – Transport/Retrans NLOS LS

SUGV MULE w/ GSTAMIDS

Trucks/Trailers:

HMMWV (C2) HMMWV (SPT) HEMTT – LHS HEMTT – Fueler HMMWV – CMT HMMWV - AMB HEMTT Wrecker Trailers – PLS FRS (Includes LHS) Tank Racks (POL) Hippos Camels SATS Trailers

Trucks/Trailers:

HMMWV (C2) HMMWV (SPT) HEMTT – LHS HEMTT – Fueler HMMWV – CMT HMMWV - AMB HEMTT Wrecker Trailers – PLS FRS (Includes LHS) Tank Racks (POL) Hippos Camels SATS Trailers

UAVs: UAV CL I L/C Units UAV CL I Aerial Vehicles UAV CL II L/C Units UAV CL II Aerial Vehicles UAV CL III L/C Units UAV CL III Aerial Vehicle UAV CL IVa L/C Units UAV CL IVa Aerial Vehicles

UAV CL IVb L/C Units

Other: RAH-66 Comanche (or alternate) 81mm Mortar Forklift – 10K Forklift – 4K E-Q36 Radar Q64 Radar AAFARS HTARS

UAVs: UAV CL I L/C Units UAV CL I Aerial Vehicles UAV CL II L/C Units UAV CL II Aerial Vehicles UAV CL III L/C Units UAV CL III Aerial Vehicle UAV CL IVa L/C Units UAV CL IVa Aerial Vehicles

UAV CL IVb L/C Units

Other: RAH-66 Comanche (or alternate) 81mm Mortar Forklift – 10K Forklift – 4K E-Q36 Radar Q64 Radar AAFARS HTARS

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Caspian Sea 20 – Vignette Overview

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SEAS FCS-BCT Scenario Forces

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Space Radar

• Built-in Sat files– SR1-SR12– Sensor "BluIMINT_narrow“

• Min_Range 0• Break_Range 2600• Max_Range 2600• Az_Width 1• El_Min -.50• El_Max .50• TLE 10

– Comm "BlueSatRadio_Tr"– Comm "BlueSatRadio_Rc"

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ISR Collection Scheduler

• Modeled in TPL as a Scheduler vehicle

• Tasks are read from external file

• Read into an array through TPL

• Prioritized based upon user priority variable

• Predetermined task duration

• If no active task is in the queue, then SRs look at default locs

• Broadcasts LookLoc to the SRs

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11/23/14

Comparative ResultsSMDS Study

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SMDC Study Test Cases

- Alt 2 and Alt 3 (no Sensor Fusion) were not run- Exc 2 (Limited Assuredness) was run for 50%, 10%, and 1% Assuredness- Exc 3 (Partial Constellation) was run for 6 radars, 3 radars, and 1 radar- An Additional Case (Exc 4) was run with No Space Radar

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Base Case

No SpaceRadar

No UAV/UGS

Box-Plot Comparison - MOU Blue Vehicles Lost & Battle Duration

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SMDC Study Comparative ResultsMOU – Avg Time to Withdraw or Defeat

Colorado Springs Co718-683-8733

11/23/14

Conclusions & RecommendationsFor Further Study

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Conclusions

• The combination of FCS-BCT organic sensors and Space Radar generate a ISR collection dynamic that increase system effectiveness.

• The combination of FCS-BCT organic sensors and Space Radar “minimize” blue casualties and battle duration. Neither alone are as effective.

• There are sensor interactions that cannot be explained by the current analysis. These require further investigation.

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Recommendations for Further Study

• Variable communication delays – sensor information latency

• Stochastic UAV survivability modeled• Non-continuous FCS-BCT UAV coverage • Global UAV ISR collection assets (e.g., Warrior)• Varying schedule algorithms – more complexity