1. Virtual Computing Environment for Future Combat Systems

2. 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

3. 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 )

4. Motivating Example – Chem-Bio Portfolio Weather, Terrain, Base map Demographics, Transportation Plume Modeling ( Images from www.fortune.com )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?

5. 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)

6. 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, 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

8. 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 N1, 50 (10) N3, 30N5, 6N4, 8 N2, 50 (5) N6, 10N7, 8 N9, 25 N8, 65 (15) N12, 18 N11, 8 N10, 30 Second Floor First Floor (7,1) (3,3) (7,1)(3,4) (5,4) (5,5) (8,1) (6,3) (6,4) (2,5) (3,1) (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

10. Result: Routes, Schedules Group of People Start timeRouteExit time IDOriginNo. of people AN860N8-N10-N134 BN861N8-N10-N135 CN830N8-N11-N144 DN130N1-N3-N4-N6-N10-N1314 EN131N1-N3-N4-N6-N10-N1315 FN132N1-N3-N4-N6-N10-N1316 GN110N1-N3-N5-N7-N11-N1414 HN210N2-N3-N5-N7-N11-N1414 IN221N2-N3-N5-N7-N11-N1415 JN222N2-N3-N5-N7-N11-N1416

11. Result – Checking edge capacity constraints N8- N11 N8- N10 N1-N3N2-N3N11- N14 N10- N13 N3-N4N3-N5N4-N6N5-N7N6- N10 N7- N11 03675 1 63 32 2 32 3 3632 4 6 32 5 32 6 32 7 8 32 9 32 10 32 11 12 13 23 14 23 15 23 Number of people move though each edge starting from each time interval

12. 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

13. Defer Assess Attack ID Decide Guidance and Objectives Detect Assess Re-attack ID Detect Locate Assess TST ID Decide Attack Detect Locate Assess ISR Detect Locate ID Locate Decide Employ 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

14. 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

15. 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 => 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)! Nest locationsDistance to open water Vegetation durability Water depth

17. 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. 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

19. 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