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Tracking Multiple Evolving Threats With Cluttered Surveillance Observations

Citation Author(s):
Zachariah Sutton, Yaakov Bar-Shalom
Submitted by:
Peter Willett
Last updated:
28 June 2018 - 7:18am
Document Type:
Research Manuscript
Document Year:
2018
Event:
Presenters Name:
Peter Willett

Abstract 

Abstract: 

Many threats in the form of human actions (terrorist attacks, military actions, etc.) can be stochastically modeled by someone with relevant expert knowledge. In this work, a threat is taken to be a modeled sequence of actions that evolve over time and culminate at some ultimate goal. A model would be a hypothesis as to how a threat would develop, and what kind of observable evidence it would produce along the way. This modeling method allows us to attempt detection using the preliminary evidence of a threat. This would theoretically allow the user to take preemptive action; i.e., the user can intercede before its culmination. This work presents a method of stochastically modeling these types of processes using Hidden Markov Models (HMMs). We then present a detection scheme based on random finite set (RFS) filters (Bernoulli filters) that allows for detection of multiple threat processes using a single cluttered stream of observed data.

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