CRESST ONR/NETC Meetings, July July, 2003 ONR Advanced Distributed Learning Bill Kaiser UCLA/SEAS Wireless Networked Sensors for Assessment  2003 Regents of the University of California

CRESST ONR/NETC Meetings, July 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.

CRESST ONR/NETC Meetings, July 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)

CRESST ONR/NETC Meetings, July 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

CRESST ONR/NETC Meetings, July Data Fusion Strategy behavioral primitive sensor data atomic-level measure sensor data atomic-level measure construct Inferential (e.g., sentries have detected and evaluated threat) GPS location, sound, images Location, numbers,threat characteristics, are threat personnel carrying weapons Descriptive (e.g., observing threat, tracking threat)

CRESST ONR/NETC Meetings, July 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

CRESST ONR/NETC Meetings, July Force Protection Training Assessment 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

CRESST ONR/NETC Meetings, July 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

CRESST ONR/NETC Meetings, July 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)

CRESST ONR/NETC Meetings, July 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

CRESST ONR/NETC Meetings, July 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