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Underwater Tactical Operations Center (UTOC)

Underwater Tactical Operations Center (UTOC). David A. Toms Mercury Computer Systems Dtoms@mc.com. Outline. Trends in tactical C4ISR: 1991: TOCs can’t get enough intel support 2002: TOCs are inundated with intel products Targeting timelines are increasing!

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Underwater Tactical Operations Center (UTOC)

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  1. Underwater Tactical Operations Center(UTOC) David A. Toms Mercury Computer Systems Dtoms@mc.com

  2. Outline • Trends in tactical C4ISR: • 1991: TOCs can’t get enough intel support • 2002: TOCs are inundated with intel products • Targeting timelines are increasing! • New tools are required to process the data • USAF/USA TOCs are migrating toward mobile, lightweight, open architectures • Technology demonstrations at Mercury • Intelligent Bandwidth Data Compression • Aided Target Recognition • Multi-Hypothesis Target Tracking • Geo-registration • Open Wings • A new architecture for tactical op centers

  3. UTOC ISR System Collection Platforms UTOC Processing Processing Processing (Sensor) Tasking Processing Exploitation Dissemination Military Convoys ‘Common Operational Picture’ Massed Forces High-Value Targets • Function of Underwater Tactical Operations Center • Provide Surveillance Operator with Unified Tactical Picture via GIG • Tools to Review Sensor Information, Provide Contextual Information • Remove Sensor Clutter, Fuse Target Information

  4. PD Assisted Exploit. ATR only IA only FAR UTOC Processing How Well do IAs Classify Targets? • ‘TPED Crisis’ • Trained Image Analyst can Process 10-30 Full-Scene 1Mega-pixel SAR Images/Hr (Global Hawk Will Generate ~6,000 Images/Hour) • Trained Surveillance Operator can Track 3-6 Targets (Joint STARS will Generate 10,000 Target Reports per second) • Trend • Semi and Fully Automated Tracking, Registration, ATR, Data Fusion • Semi-Automated Situation Assessment, Sensor Tasking Dr. John M. Irvine, SAIC Presentation at ATR Transition Conference, MIT Lincoln Lab, June 7, 2000 TPED: Tasking, Processing, Exploitation, and Dissemination PD: Probability of Detection, FAR: False Alarm Rate

  5. Processing Algorithm Impact Type Throughput Model Geometry Rule-Based Situation Assessment Physics Statistical Intelligence PatternAnalysis Knowledge Processing Increasing Desired Information Decreasing Amount of Data Information Processing Fusion ATR Data Processing 10 - 100 GFLOPS Mercury Domain Tracking Detection Signal Processing 100s of GFLOPS Filtering Amount of Sensor Data (100s of MB/s) Processing Algorithms

  6. Signal and Data Processing Architecture ‘Data’ Processing Sensor Mode/ Look Region Signal Processing Report Processing Signal Analysis Registration Tracking Sensor Tasking ESM MTI TargetDetection MTIRegistration Multi-Sensor/ Multi-LookATR Fusion Fire ControlSystems MTI Signal Processing (e.g., STAP) HRR FeatureExtraction 1D ATR Image Change Detection Radar Signal SAR ImageFormation SAR Registration SAR Target Detection SAR FeatureExtraction 2D ATR Video/IRRegistration TargetDetection FeatureExtraction ATR Video/IR Signal Image-Level Processing Chip-Level Processing Image Processing

  7. UTOC Data Fusion Types of Sensor Data to be fused: • Synthetic Aperture Radar imagery • Ground Moving Target Indicator reports • Electro-optic/Infrared imagery • Hyperspectral imagery • SIGINT/ELINT reports • COMINT data • BDA • Chem/Bio/WMD reports • Minefield delimitations • Unattended sensors

  8. Stand-Alone Demonstrations • Tools are required to analyze all the data • Completed demonstrations: • Intelligent Bandwidth Compression (Sandia Labs/Black River Systems/Mercury) • Model-based ATR Algorithm (DARPA) • Geo-Registration (DARPA) • Under Development • Multiple Hypothesis Tracking (DARPA)

  9. Intelligent Bandwidth Compression • Full-Scene Image Compressed After Target Chips Have Been Detected • High Ratio for Background • Low Ratio for Target Chips • Overall Ratio ~128:1

  10. MTI REPORTS TRACKS GMTI Tracking Function • Generate Target Tracks Based on MTI Radar Reports • Tracker has to Account for • Non-constant Target Velocities • Measurement Errors • Missed Detections • False Reports

  11. GMTI Tracking Approach RoadData Retained Track Hypotheses PREDICTTRACKS Time • Multitarget Tracking • Associate Reports for Tracks • Filter out Measurement Noise by Averaging • Multiple Hypothesis Approach (MHT) • Form Multiple Hypotheses for Report Associations and Target Kinematics • Select Most Likely Hypothesis After Processing Multiple Frames of MTI Reports DTEDData Predicted Track Hypotheses Report/Track Pairs Updated Track Hypotheses GATE REPORTS WITH TRACKS UPDATETRACKS TRACKHYPOTHESISMANAGEMENT TargetTracks ConstrainedMTI Reports CREATENEW TRACKS PREPROCESSREPORTS MTI Reports DTEDData RoadData Search Problem

  12. Image Registration Function • Provides Spatial Correspondence Between Two Images • Image Registration Prerequisite for Performing Change Detection Registration Results: Alan Chao, Alphatech

  13. Image Geo-Registration Approach • Two-step Procedure Relative Geometry GeocodedImages RegisteredImages Images ImageGeocoding ImageRegistration Geocoding Error Statistics Registration Error Statistics System Error Statistics • Image Geocoding (Image Ortho-rectification): Image Projected to a Common Reference Frame • Image Registration: Uses Image Data (Pixel Intensities or Image Feature) to Find Spatial Correspondence Between Images Search Problem

  14. Image Registration Requirements • Function of • Feature or Pixel Intensity Approaches • Features Used (e.g., Topological, Region, Object) • Matching Algorithm (e.g., Hausdorff Distance, Bayesian Metric) • Desired Accuracy • Preliminary Estimates (Feature-Based Algorithm for SAR Image Registration) • ~450x350 Pixel SAR Image • 10 -100 Giga operations Processing Requirements are Function of Input Rate, Data Characteristics, Desired Performance

  15. ATR Function • Classify Target Based on Image Chip • 2-D ATR: Uses High-Res SAR Image Data • 1-D ATR: Uses High-Res Range Profile from Radar MTI Data (Research Area) Range-Profile 2-D ATR 1-D ATR T72 Confidence:0.95 SARTarget Chip SCUD TEL Confidence:0.7 Range-DopplerTarget Chip

  16. 2-D ATR Approach • Traditional Approach: Template-Based • Store Target SAR Templates for Various Poses, Articulation • Find Best Match and Declare Target Type • Model-Based Approach (Research) • Store Wire-Frame Model for Various Target Types • Predict, Evaluate, Match, Search for Best Match of Pose and Articulation

  17. T72 T72 BTR70 SA8 ZSU T72 ZSU BTR70 SCUD T72 Ground Truth System call & score, if correct System call & score, if incorrect T72 .90 T72 .95 BTR70 .85 SA8 .80 ZSU .95 T72 .95 T72 .65 BTR70 .80 SCUD .90 ZSU BTR70 SA8 BTR70 ZSU ZSU SCUD SA8 ZSU T72 T72 T72 BTR70 SA8 ZSU T72 ZSU BTR70 T72 BTR70 Click on image chip to inspect ATR details 2-D ATR Requirements • Preliminary Estimates • From Demo System • Chip-Level Parallel Processing • Approx. 40 Mega operations per Chip

  18. Model-Based ATR Algorithm Detect Cue ROIs Focus of Attention Index SAR Image Type (x,y)  Score M2 35,87 10 0.91 M548 38,88 14 0.83 BMP2 32,89 192 0.05 ... Coarse Hypotheses Explain Scene Model Scene Hypothesis ROI Search Evaluations Predict Extract Predicted Features Extracted Features Verify Match

  19. Mercury Role in UTOC • Established Leader in Signal Processing • Expertise in Sensor Algorithm Technology • Middleware to Support Application Development • Low space, weight and power requirements • Mercury HPC Architecture Well-Suited for Data Processing • Can be Scaled to Support Improvements in Sensor Resolution • Supports Algorithms Requiring Tight Coupling Between Signal & Data Processing Functions • Testing underway at WPAFB Sensor laboratory

  20. Openwings Architecture DomainExample Container Specifications HPC UTOC ProcessingRequirements Participation in Ground Station Activities: Openwings • Openwings: Architecture for Plug-and-Play, Network Centric, Service Oriented System • Mobile Ground Stations is a Domain Example • Mercury is on Expert Team • Analyzing Ground Station Processing Requirements • Developing HPC Container Specifications • Life Cycle Support for an Application • Clustering of Processors • Process Load Balancing www.openwings.org

  21. Program Overview • Openwings initiative established June ‘99 as a Joint IR&D effort between Motorola & Sun • Motorola and Sun Microsystems to lead a community in the development of a distributed, self-forming architecture • Mercury will provide High Performance Computing engines • Architecture development will be done using an open development approach • Initial framework is available to the Openwings community

  22. Summary • If submarines are to become full players in network centric warfare, then accessing and exploiting all available data sources will become essential • We are performing groundwork to show Mercury’s computers can meet these requirements • Conducting data processing requirements analysis and preparing demonstrations • These tools could be used for sonar data exploitation as well.

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