TY - GEN
T1 - Countering improvised explosive devices with adaptive sensor networks
AU - Buenfil, Jorge R.
AU - Ramirez-Marquez, Jose
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/14
Y1 - 2016/9/14
N2 - The design and architecture of a system for automatic Improvised Explosive Devices detection to protect sensitive areas with minimal human interaction is presented. The system, called ACE for 'Army Counter IED Enhanced', employs a variety of statistical analysis, pattern recognition and human machine interface in conjunction with adaptive mechanisms. ACE combines four different kinds of inputs: image processing, nonvisual inputs, pattern recognition, and Kalman filters. ACE produces three kinds of outputs: A visualization of the area under surveillance, a highlight of potential threats, and alarms that trigger traffic control devices to contain the threat while security forces proceed to confirm and neutralize the threat. Data fusion of the inputs is conducted with a dynamic system assigning weights to the values provided by each input, adding results into a threat assessment value (TAV), in order to compare it to thresholds for alerts and alarms.
AB - The design and architecture of a system for automatic Improvised Explosive Devices detection to protect sensitive areas with minimal human interaction is presented. The system, called ACE for 'Army Counter IED Enhanced', employs a variety of statistical analysis, pattern recognition and human machine interface in conjunction with adaptive mechanisms. ACE combines four different kinds of inputs: image processing, nonvisual inputs, pattern recognition, and Kalman filters. ACE produces three kinds of outputs: A visualization of the area under surveillance, a highlight of potential threats, and alarms that trigger traffic control devices to contain the threat while security forces proceed to confirm and neutralize the threat. Data fusion of the inputs is conducted with a dynamic system assigning weights to the values provided by each input, adding results into a threat assessment value (TAV), in order to compare it to thresholds for alerts and alarms.
KW - Adaptive Systems
KW - Bayesian rule
KW - EOD
KW - HMI
KW - IED
KW - Kalman filter
KW - OODA loop
KW - data fusion
KW - dynamic system
KW - orthogonal sensors
KW - pattern recognition
KW - raspberry pi
KW - reinforcement learning
KW - threat assessment
UR - http://www.scopus.com/inward/record.url?scp=84991753342&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991753342&partnerID=8YFLogxK
U2 - 10.1109/THS.2016.7568923
DO - 10.1109/THS.2016.7568923
M3 - Conference contribution
AN - SCOPUS:84991753342
T3 - 2016 IEEE Symposium on Technologies for Homeland Security, HST 2016
BT - 2016 IEEE Symposium on Technologies for Homeland Security, HST 2016
T2 - 2016 IEEE Symposium on Technologies for Homeland Security, HST 2016
Y2 - 10 May 2016 through 11 May 2016
ER -