Countering improvised explosive devices with adaptive sensor networks

Jorge R. Buenfil, Jose Ramirez-Marquez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2016 IEEE Symposium on Technologies for Homeland Security, HST 2016
ISBN (Electronic)9781509007707
DOIs
StatePublished - 14 Sep 2016
Event2016 IEEE Symposium on Technologies for Homeland Security, HST 2016 - Waltham, United States
Duration: 10 May 201611 May 2016

Publication series

Name2016 IEEE Symposium on Technologies for Homeland Security, HST 2016

Conference

Conference2016 IEEE Symposium on Technologies for Homeland Security, HST 2016
Country/TerritoryUnited States
CityWaltham
Period10/05/1611/05/16

Keywords

  • Adaptive Systems
  • Bayesian rule
  • EOD
  • HMI
  • IED
  • Kalman filter
  • OODA loop
  • data fusion
  • dynamic system
  • orthogonal sensors
  • pattern recognition
  • raspberry pi
  • reinforcement learning
  • threat assessment

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