Virtual Computing Environment for Future Combat Systems

Commanders Net work

Battlefield Simula tion

National Assets, e.g. Grids, Maps, Models

Sensor NetworkShooters Network

Virtual da ta grid

Multi-body Structures

GISVisualiza tion

Virtual Prototyping

Applications to Future Combat Systems

VCS Te chnologie s

HPGIS

Commanders Network e.g. Situation Assessment

National Assets, e.g. Maps

Sensor Network

Shooters Network

Maps are as important to soldiers as guns

Example Usage of Geographic Info. Systems (GIS) in Battlefield :

•Rescue of pilots after their planes went down (recently in Kosovo)

•Precision targeting e.g. avoid civilian casualities (e.g. friendly embassies)

•Logistics of Troop movements, avoid friendly fires

Motivating Example – Urban Warfare

Mogadishu, Somalia, 10/3/1993 Soldiers trapped by roadblocks No alternate evacuation routes Rescue team got lost in alleys having no planned route to crash site 18 Army Rangers and elite Delta Force soldiers killed, 73 wounded.

“Black Hawk Down”

( Mark Bowden, Black Hawk Down: A Story of Modern War )

Motivating Example – Chem-Bio Portfolio

Weather, Terrain, Base map

Demographics, Transportation

Plume

Modeling

( Images from www.fortune.com )

• Examples• Chem-Bio portfolio project (Dr. Alibadi)• Scenario – managing a (say chem-bio) attack

• Components of the system• Gathering initial conditions

• Weather data from NWS or JSU • Terrain maps (State of federal Govt.)• Building geometry (City Govt.)

• Plume simulation using supercomputers• Visualizing results – map, 3D graphics• Response planning

• Q? What happens after plume simulation, visualization?

Homeland Defense: Chem-Bio Portfolio

Hurrican Andrew, 1992 Traffic congestions on all highways Great confusions and chaos

"We packed up Morgan City residents to evacuate in the a.m. on the day that Andrew hit coastal Louisiana, but in early afternoon the majority came back home. The traffic was so bad that they couldn't get through Lafayette."

- Morgan City, Louisiana Mayor Tim Mott

( http://i49south.com/hurricane.htm )

( National Weather Services)

( www.washingtonpost.com)

Problem Statement

Given• Transportation network (e.g. building floor map, city roadmap) with

capacity constraints• Initial number of people to be evacuated and their initial location • Evacuation destinations

Output• Scheduling of people to be evacuated and the routes to be taken

Objective• Minimize total time needed for evacuation• Minimize computational overhead

Constraints• Capacity constraints: evacuation plan meets capacity of the network

Route Algorithm - Related Works

• Dynamic network flow (Ford and Fulkerson, 1960’s)– Quickest Flow Problem: Only apply to single source and single destination node

• Simple algorithms for multiple source and destination (1970’s-1980’s)– Algorithms have exponential running time, e.g. EVACNET(University of Florida)

• Improved algorithms (1990’s)– Klinz:

• Polynomial time algorithm• Can only find required time, not the evacuation plan

– Tardos(1994): • Polynomial time algorithm to find optimal plan for fixed number of sources • Cannot apply to variable number of sources• Cannot apply to variable arc capacity, e.g. arc capacity changed over time• May produce fractional solution, e.g. “5.2 people go to …”,

feasible evacuation plan requires integer solution

Route Algorithm - Our Approach

• Algorithm Design– Extend shortest path algorithms (e.g. A*) To honor capacity contraints

– Attach a time-series with each node and edge

• Edge capacity

• Node occupancy

– Start single-source routing between all (source, dst) pairs• First route found is used to reduce edge and node attributes

• Process repeats till node capacities are reduced to zero

• Evaluation– Much faster than the current approaches

– Solution quality is comparable on hand tested examples• Problems with little interference across routes, ;arge edge capacities

– Detailed evaluation in progress

Example Map

N1, 50(10)

N3, 30 N5, 6N4, 8

N2, 50(5)N6, 10 N7, 8

N9, 25

N8, 65 (15)

N12, 18

N11, 8N10, 30

Second Floor

First Floor

(7,1)

(3,3)(3,3)

(7,1) (3,4)(5,4)

(5,5)

(8,1) (6,3)

(6,4)

(6,4)

(6,4) (2,5)

(3,1)

(3,3)

(3,3)

(14,4)

(Max Capacity, Travel time)

Node ID, Max Capacity(Initial Occupancy)

EXIT #2

EXIT #1

N13

N14

Node ID

Exit

Node

Edge

Result: Routes, Schedules

Group of PeopleStart time Route Exit time

ID Origin No. of people

A N8 6 0 N8-N10-N13 4

B N8 6 1 N8-N10-N13 5

C N8 3 0 N8-N11-N14 4

D N1 3 0 N1-N3-N4-N6-N10-N13 14

E N1 3 1 N1-N3-N4-N6-N10-N13 15

F N1 3 2 N1-N3-N4-N6-N10-N13 16

G N1 1 0 N1-N3-N5-N7-N11-N14 14

H N2 1 0 N2-N3-N5-N7-N11-N14 14

I N2 2 1 N2-N3-N5-N7-N11-N14 15

J N2 2 2 N2-N3-N5-N7-N11-N14 16

Result – Checking edge capacity constraints

N8-N11

N8- N10

N1-N3 N2-N3 N11-N14

N10-N13

N3-N4 N3-N5 N4-N6 N5-N7 N6-N10

N7-N11

0 3 6 7 5                

1   6 3       3 2        

2             3 2        

3         3 6 3 2        

4           6     3 2    

5                 3 2    

6                 3 2    

7                        

8                     3 2

9                     3 2

10                     3 2

11                        

12                        

13         2 3            

14         2 3            

15         2 3            

Number of people move though each edge starting from each time interval

Routing – Next Phase (S. Shekhar)

• AHPCRC Relevance – Projectile Target Interaction Portfolio– Increase lethality of weapons such as guided missiles

– Pre-lauch routing – stealth route avoiding enemy sensor network

– In-route routing • to correct drifts from planned trajectory

• To route route unanticipated obstacles

• Possible Extensions in 2002-2003– Focus on relevance to AHPCRC Portfolios

– Complete design and implementation of routing algorithm with capacity constraints

– Performance evaluation with real datasets

Defer

Assess Attack

ID

Decide Guidance and

ObjectivesDetect

Assess Re-attack

ID

Detect

LocateAssess TST ID

Decide Attack

Detect

LocateAssess ISR

Detect

Locate

ID

Locate

DecideEmploy wpns

• Iterative process driven by effort to refine data about target ID, location, and status

• Process timeline compresses for TSTs

• Process necessarily balances timeliness, lethality, and accuracy

SPIRAL NATURE OF THE PRECISION ENGAGEMENT PROCESS

Target

Decide TST

Status

Location Prediction and Spatial Data Mining (S. Shekhar)

• Specific Project in 2001-2002– Evaluation of location prediction techniques

– Towards high performance parallel implementation

• AHPCRC Relevance – Projectile Target Interaction Portfolio– Increase lethality of weapons such as guided missiles

– Location prediction for map matching• to check correctness of missile trajectory

• To identify unanticipated obstacle

– Towards possible rerouting

• Army Relevance in general– Predicting global hot spots (FORMID)

– Army land management endangered species vs. training and war games

– Search for local trends in massive simulation data

– Critical infra-structure defense (threat assessment)

– Inferring enemy tactics (e.g. flank attack) from blobology

– Locating enemy (e.g. sniper in a haystack, sensor networks)

– Locating friends to avoid friendly fire

Accomplishments

• Formal Results• SAR - parametric statistics, provides confidence measures in model

• MRF from non-parametric statistics

• SAR : MRF-BC :: linear regression : Bayesian Classifier

• Rewrite SAR as y = (QX) + Q, where Q = (I- W)-1

• SAR has linear class boundaries in transformed space (QX, y)

• MRF-BC can represent non-linear class boundaries

• Experimental results • MRF-BC can provide better classification accuracies than SAR

• But solution procedure is very slow

• Details in Recent paper in IEEE Transactions on Multimedia

Location Prediction

• Problem Definition: Given: 1. Spatial Framework

2. Explanatory functions:

3. A dependent function:

4. A family of function mappings:

Find: A function

Objective: maximize classification accuracy

Constraints: Spatial Autocorrelation in dependent function

• Past Approaches: Non-spatial: logistic regression, decision trees, Bayesian

– Assume independent distribution for learning samples

– Auto-correlation => poor prediction performance

Spatial: Spatial auto-regression (SAR), Markov random field Bayesian classifier (MRF)

– No literature comparing the two!

– Learning algorithms for SAR are slow (took 3 hours for 5000 data points)!

},...{ 1 nssS

RSfkX

:

}1,0{: SfY

}1,0{... RRyf̂),ˆ( yy ff

Nest locations Distance to open water

Vegetation durability Water depth

Accomplishments

• Formal Results• SAR - parametric statistics, provides confidence measures in model

• MRF from non-parametric statistics

• SAR : MRF-BC :: linear regression : Bayesian Classifier

• Rewrite SAR as y = (QX) + Q, where Q = (I- W)-1

• SAR has linear class boundaries in transformed space (QX, y)

• MRF-BC can represent non-linear class boundaries

• Experimental results • MRF-BC can provide better classification accuracies than SAR

• But solution procedure is very slow

• Details in Recent paper in IEEE Transactions on Multimedia

• Scaleable parallel methods for GIS Querying for Battlefield Visualization

• A spatial data model for directions for querying battlefield information

• Spatial data mining: Predicting Locations Using Maps Similarity (PLUMS)

•An efficient indexing method, CCAM, for spatial graphs, e.g. Road Maps

Past Accomplishments

• High Performance Geographic Information Systems (HPGIS)

– Parallel formulations for terrain visualization

– Efficient storage (e.g. CCAM), join-index

• More expressive GIS - Query languages, Data models

– Mobile objects, Direction and Orientation

– Processing direction based queries

• Smarter GIS - Spatial Data Mining

– Spatial prediction, classification

– Association among spatial features

– Spatial outlier detection

GIS Research at AHPCRC