Target Tracking Applied to Extraction of Multiple Evolving Threats From a Stream of Surveillance Data

Abstract

Many threats (terrorist attacks, military actions, etc.) can be modeled by someone with relevant expert knowledge. A “threat” here implies a sequence of actions that evolve over time and are intended to culminate in a goal that from the article’s perspective is unfavorable. This work presents a method to model probabilistically these types of processes using hidden Markov models (HMMs). We thence present a detection scheme based on random finite set (RFS) filters—specifically a multi-Bernoulli approach—that allows for detection of multiple threat processes using a single observed data stream. Key here is that associated with threats are a list of entities that are a priori unknown and must be inferred, but once (probabilistically) identified, aid greatly in the data association step, and inference on the perceived threat.