Sequential threat detection for harbor defense: An x-ray physics-based bayesian approach

Abstract

The timely and accurate detection of threat contraband especially for ports-of-entry (e.g. harbors, bays, borders, airports) is an extremely critical problem of national security. The investigation of advanced techniques to reliably and accurately detect threats and reject non-threats is the major focus of this effort. The characterization of signal processing models based on xray transport physics is a crucial element in advanced sequential Bayesian processor designs. Incorporating the underlying statistics of x-ray interactions with materials offering a potentially unique signature of an object or item under investigation leads to a (stochastic) physics-based approach. State-space models, common in many application areas, are introduced into the x-ray radiation area. Here the resulting processor incorporating this construct is developed from a pragmatic perspective. A Gaussian application is discussed to illustrate feasibility of the overall physics-based approach. It is shown that the sequential Bayesian processor is capable of providing a reliable and accurate solution with high confidence in a timely manner for this problem based on a set of synthesized object intensity data.