Potential IED threat system (PITS)

Li, Hua; Bramsen, Diane; Alonso, Rafael

doi:10.1109/ths.2009.5168041

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…A variety of methods have been used for assessing likelihoods of IEDs at a location (Fong and Zhuang [8]; Li, Bramsen, and Alonso [9]; Parunak, Sauter, and Crossman [11]). Poisson models can work well, but the base rate will vary considerably with the terrain.…”

Section: Prior Probabilities For Emplacementmentioning

confidence: 99%

Networks of simple sensors for detecting emplacement of improvised explosive devices

Rowe¹,

Reed²,

Schwamm³

et al. 2012

Critical Infrastructure Security

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Detection of improvised explosive devices is difficult and requires a wide spectrum of strategies. Detection during emplacement is the best hope. Nonimaging sensors provide several advantages over cameras in expense, robustness, and processing simplicity for this task. We describe experiments with inexpensive commercial sensors, and show how data can be combined to provide monitoring for suspicious pedestrian behavior at a 1-10 meter scale. Our approach preanalyzes terrain to rate likelihood of emplacement. We install sensors and monitor the terrain, seeking direct clues to suspicious behavior such as loitering and odd sounds such as excavation. We also use sensor data to track people by inferring their probability distributions, and use this to detect significant accelerations and atypical velocity vectors, both of which can indicate suspicious behavior. We describe experiments we have conducted with a prototype sensor network of eight kinds of sensors, from which it appears that motion and sonar sensors are the most helpful for this task.

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Section: Prior Probabilities For Emplacementmentioning

confidence: 99%

Networks of simple sensors for detecting emplacement of improvised explosive devices

Rowe¹,

Reed²,

Schwamm³

et al. 2012

Critical Infrastructure Security

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show abstract

“…The PITS++ system is based on its predecessor PITS system [4], which computes these influence maps based on purely geographical features such as the (x,y) location of the map. Our goal is to enhance the influence map by adding non-geographical features such as time or income level in the area of the attacks.…”

Section: Figure 1: Pits++ Viewer Showing the Training (Cyan) And Testmentioning

confidence: 99%

“…The PITS++ tool is built on our previous work on PITS [4]. Note that the main difference between the two systems is that PITS++ incorporates a CBR mechanism into the original PITS system.…”

Section: Pits++: Function Value Estimation Predictionmentioning

confidence: 99%

“…Note that the main difference between the two systems is that PITS++ incorporates a CBR mechanism into the original PITS system. Figure 2 provides an overview of the original PITS system function value estimation mechanism [4]. The inputs to PITS include terrain, IED events, and a history of friendly (blue) and opponent (red) force activity.…”

Section: Pits++: Function Value Estimation Predictionmentioning

confidence: 99%

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Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning

Li¹,

Muñoz-Ávila

Bramsen³

et al. 2009

Case-Based Reasoning Research and Development

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Abstract. This paper presents a new approach for spatial event prediction that combines a value function approximation algorithm and case-based reasoning predictors. Each of these predictors makes unique contributions to the overall spatial event prediction. The function value approximation prediction is particularly suitable to reasoning with geographical features such as the (x,y) coordinates of an event. The case-based prediction is particularly well suited to deal with non-geographical features such as the time of the event or income level of the population. We claim that the combination of these two predictors results in a significant improvement of the accuracy in the spatial event prediction compared to pure geographically-based predictions. We support our claim by reporting on an ablation study for the prediction of improvised explosive device (IED) attacks.

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Potential IED threat system (PITS)

Cited by 2 publications

References 7 publications

Networks of simple sensors for detecting emplacement of improvised explosive devices

Networks of simple sensors for detecting emplacement of improvised explosive devices

Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning

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