Virtual Computing Environment for Future Combat Systems

1
Virtual Computing Environment for Future Combat
Systems
2
Maps are as important to soldiers as guns
Shooters Network
Sensor Network
HPGIS
Commanders Network e.g. Situation Assessment
National Assets, e.g. Maps

• 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

3
Motivating Example Urban Warfare
Black Hawk Down

• 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.

( Mark Bowden, Black Hawk Down A Story of Modern
War )
4
Motivating Example Chem-Bio Portfolio

• 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?

Weather, Terrain, Base map
Plume Modeling
Demographics, Transportation
( Images from www.fortune.com )
5
Homeland Defense Chem-Bio Portfolio
"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)

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

( www.washingtonpost.com)
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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

7
Route Algorithm - Related Works

• Dynamic network flow (Ford and Fulkerson, 1960s)
• Quickest Flow Problem Only apply to single
  source and single destination node
• Simple algorithms for multiple source and
  destination (1970s-1980s)
• Algorithms have exponential running time, e.g.
  EVACNET(University of Florida)
• Improved algorithms (1990s)
• 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

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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

9
Example Map
Node
N1, 50 (10)
Node ID, Max Capacity (Initial Occupancy)
(7,1)
N3, 30
N5, 6
N4, 8
(3,3)
(3,3)
Edge
(Max Capacity, Travel time)
(7,1)
(3,4)
(5,4)
Exit
N2, 50 (5)
N6, 10
N7, 8
Node ID
Second Floor
N9, 25
First Floor
(2,5)
(5,5)
(6,4)
(14,4)
(6,4)
EXIT 1
N11, 8
(8,1)
(3,3)
N10, 30
(6,3)
N8, 65 (15)
N13
(3,1)
(6,4)
N12, 18
(3,3)
N14
EXIT 2
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Result Routes, Schedules
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Result Checking edge capacity constraints
Number of people move though each edge starting
from each time interval
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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

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SPIRAL NATURE OF THE PRECISION ENGAGEMENT PROCESS
Locate

• 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

Assess ISR
Locate
Defer
ID
Locate
Assess TST
ID
ID
Locate
Assess Attack
ID
Decide Attack
Assess Re-attack
Decide Employ wpns
Detect
Decide Guidance and Objectives
Target
Decide TST Status
Detect
Detect
Detect
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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

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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

16
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 gt 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)!

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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

18
Past Accomplishments

• 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

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GIS Research at AHPCRC

• 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