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Ships involved in commercial activities tend to follow set patterns of behaviour depending on the business in which they are engaged. If a ship exhibits anomalous behaviour, this could indicate it is being used for illicit activities. With the wide availability of automatic identification system (AIS) data it is now possible to detect some of these patterns(More)
This paper describes a model and algorithm for detecting anomalies in track data. The algorithm is general in the sense that it can be applied to tracks from any type of sensor. Two important enhancements to the standard algorithm are outlined. The first of these characterizes predictable temporal variations in behavior, the so-called rhythm of life. This(More)
This paper presents a numerical Bayesian approach to the autofocus and super-resolution of targets in radar imagery. An ill-posed inverse problem is studied in which the known linear imaging operator is subject to an unknown degree of distortion (defocusing). The goal is simultaneously to reconstruct a high-resolution representation of a target based on(More)
This paper presents a framework for target acquisition. The targets of interest are relocatable ground vehicles imaged at time t=t 0 by a long range targeting sensor and then at a later time t=t 1 by a weapon platform. The framework must handle several key issues: changes in scene (vehicle movement between t 0 and t 1); incorporation of domain knowledge(More)
This paper considers an automatic target recognition (ATR) application in which a targeting sensor is used to guide a seeker-equipped weapon to an area containing high-value relocatable targets. The weapon seeker then needs to engage the high value targets, while minimising collateral damage. A Bayesian approach is proposed that enables the weapon seeker to(More)