Asymmetric Threat Modeling Using HMMs: Bernoulli Filtering and Detectability Analysis
Artikel i vetenskaplig tidskrift, 2016
There is good reason to model an asymmetric threat (a structured action such as a terrorist attack) as an HMM whose observations are cluttered. Within this context, this paper presents two important contributions. The first is a Bernoulli filter that can process cluttered observations and is capable of detecting whether there is an HMM present, and if so, estimate the state of the HMM. The second is an analysis of the problem that, for a given HMM model, is able to make statements regarding the minimum complexity that an HMM would need to involve in order that it be detectable with reasonable fidelity, as well as upper bounds on the level of clutter (expected number of false measurements) and probability of miss of a relevant observation. In a simulation study, the Bernoulli filter is shown to give good performance provided that the probability of observation is larger than the probability of an irrelevant clutter observation. Further, the results show that the longer the delays are between the HMM state transitions, the larger the probability margin must be. The feasibility prediction shows that it is possible to predict the boundary between poor performance and good performance for the Bernoulli filter, i.e., it is possible to predict when the Bernoulli filter will be useful, and when it will not be.
hidden Markov model