The main focus of this work is on developing models for the activity profile
of a terrorist group, detecting sudden spurts and downfalls in this profile,
and, in general, tracking it over a period of time. Toward this goal, a
$d$-state hidden Markov model (HMM) that captures the latent states underlying
the dynamics of the group and thus its activity profile is developed. The
simplest setting of $d=2$ corresponds to the case where the dynamics are
coarsely quantized as Active and Inactive, respectively. A state estimation
strategy that exploits the underlying HMM structure is then developed for spurt
detection and tracking. This strategy is shown to track even nonpersistent
changes that last only for a short duration at the cost of learning the
underlying model. Case studies with real terrorism data from open-source
databases are provided to illustrate the performance of the proposed
methodology.

本论文研究了恐怖组织活动轮廓建模的方法，发展了一个基于 d 状态的隐式马尔科夫模型来追踪恐怖组织的活动轮廓，提出了一种旨在探测和追踪恐怖组织活动突发事件的状态估计策略，并通过实验表明，该方法在非持续的行为变化上有着良好的表现。