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ONR Advanced Distributed Learning. Wireless Networked Sensors for Assessment. 17 July, 2003. Bill Kaiser UCLA/SEAS. 2003 Regents of the University of California. Objective. Fundamental advances in assessment for primary mission needs Force Protection Readiness Damage Control

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17 july 2003

ONR Advanced Distributed Learning

Wireless Networked Sensors for Assessment

17 July, 2003

Bill Kaiser

UCLA/SEAS

2003 Regents of the University of California


Objective
Objective

  • Fundamental advances in assessment for primary mission needs

    • Force Protection Readiness

    • Damage Control

    • Weapon Training

  • Enabled by simultaneous advances in automated reasoning and Networked Embedded Sensing

  • Now include physical environment and real events into assessment.


Exploiting past progress
Exploiting Past Progress

  • First wireless sensor programs

    • Low Power Wireless Integrated Microsensors (LWIM)

      • DARPA/MTO (1994)

    • Cooperative Wireless Sensors

      • DARPA/ATO (1997)

    • Distributed Wireless Imager Sensors

      • FAA (1998)

    • Distribute Environmental Sensors

      • JPL Global WINS (1999)

  • 20 field deployments

    • Navy Ship USS Rushmore for condition monitoring

    • Many deployments at 29 Palms MCAGCC for Force Protection (DESFIREX, Steel Knight, and others)


Networked embedded sensing technology status
Networked Embedded Sensing Technology Status

  • Distributed wireless sensors and embedded computing

  • Multihop, scalable networking

  • Wearable devices and systems that may monitor assets (vehicle, weapons, environment)

  • Now possible to host CRESST assessment algorithms jointly on remote devices sensing physical environment and centralized server systems


Data fusion strategy
Data Fusion Strategy

Inferential (e.g., sentries have detected and evaluated threat)

construct

behavioral primitive

Descriptive (e.g., observing threat, tracking threat)

• • •

Location, numbers,threat characteristics, are threat personnel carrying weapons

atomic-level measure

atomic-level measure

• • •

sensor data

sensor data

GPS location, sound, images


Force protection training assessment
Force Protection Training Assessment

  • Assess capability of equipment, personnel, and perimeter protection methods

    • Monitor threats (personnel and vehicles)

      • Monitor location and behavior

    • Monitor environment

      • Determine what threat signatures may be detected by personnel

    • Monitor personnel

      • Map response to events (action, time of action)

      • Determine influence of fatigue, confusion, distraction

      • Determine vulnerabilities to specific threat actions


Force protection training assessment1
Force Protection TrainingAssessment

  • Implementation

    • Distributed, wireless, embedded computing incorporated with threat vehicles and personnel, environment, and sentry personnel under assessment

    • Embedded Nodes

      • Wireless Communication

      • Location

      • Inertial Sensing

      • Environmental Sensing (acoustic and image sensing)

    • Nodes Deployment

      • Incorporated with threat elements for location and behavior

      • Placed in environment for detection of signals

      • Monitoring of and worn by force protection personnel


Damage control
Damage Control

  • Assess capability of equipment, personnel, and damage evaluation and control methods

    • Instrument equipment and environments

      • Monitor damage

    • Instrument environment

      • Determine what damage signatures may be observed by personnel

    • Monitor personnel response

      • Map response to events (action, time of action)

      • Determine influence of fatigue, distraction, visibility, injury, and other factors on performance and adherence to procedure


Weapons training
Weapons Training

  • Assess capability of specific weapon skills

    • Compact instruments applied to weapon

    • Wearable monitoring device provides local processing, storage, and networking

    • Monitor biomedical parameters

    • Monitor environmental conditions

    • Monitor physical weapon parameters (vibration, pointing stability, and others)


System architecture
System Architecture

  • Remote embedded platforms

    • Wireless network capability for access to remotes

    • Local signal processing

    • Local evaluation

  • Centralized server platform

    • Bayesian network processing of sensor responses

  • Develop individualized model for personnel mission readiness

    • Evaluate and ultimately optimize procedures, equipment, environment


Pilot program
Pilot Program

  • Rapid evaluation and approach optimization in one year program

    • Force Protection

    • Damage Control

    • Weapons Training

  • Demonstrate capability and benefits of merged automated reasoning and embedded sensing approach

  • Deliver assessment results and plan for end-to-end system implementation


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