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Sniper Detection Sniper Detection Using Wireless Sensor Using Wireless Sensor

NetworksNetworksJoe BrassardJoe Brassard

Wing SiuWing SiuEE-194WIR: Wireless Sensor NetworksEE-194WIR: Wireless Sensor Networks

Presentation #1: February 10, 2005Presentation #1: February 10, 2005

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Project Goals/Schedule Research the theory behind sensor network

based counter-sniper systems Examine early implementations (Viper,

Bullet Ears, Pilar, LifeGuard) Study a newer system recently developed

at Vanderbilt University (PinPtr) Suggest possible improvements to these

systems

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Sniper Detection By definition a sniper is usually hidden from

view

Need sensors and software to track position post-shot

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Sniper Detection Ways to track a bullet

Visually – Infrared Cameras Acoustically - Microphones Shockwave – Pressure sensors

Automated detection systems use some combination of these factors

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Some Sniper Detection Systems

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A Rifle Shot Rifle bullet travels at supersonic speeds,

creating its own shockwave and loud muzzle blast

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Finding the Sniper Record physical phenomena

Projectile thermal signature Muzzle flash Muzzle blast and resultant shockwave

Multiple sensors work best Accuracy Multiple issues

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Infrared Detection Infrared image vs. “visible” image

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Acoustic/Shockwave Detection Rifle shot generates acoustic events

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Sensor Network Issues Time Synchronization Message Routing Sensor Localization/Density Signal Detection Portability Dynamic/Static Sensor Networks Processing Sensor Information

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Time Synchronization Issues To obtain reference

points from each sensor, a time synchronization protocol is necessary

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Sensor Configuration Issues How should sensors

be placed? Sensor density needs

to be considered Density decreases over

time Line-of-Sight for muzzle

blast detection needed Projectile trajectory must

not be shaded for shockwave detection

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Signal Detection Issues Sensitivity of microphones must be very low

in order to handle muzzle blasts and shockwaves (to avoid false positives)

Shockwave and muzzle blast events need to be individually modeled as each sensor might not register both signals each shot

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Finished Product One Implementation For Sniper Detection:

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References Maroti, et. al. “Sensor Network-Based

Countersniper System” (Vanderbilt University) Vick, et. Al. “Aerospace Operations in Urban

Environments” (RAND) “LifeGuard” Video Courtesy of Lawrence

Livermore National Laboratory (University of California)

BBN Technologies FLIR Systems, Inc.