In maritime operational scenarios, such as smuggling, piracy, or terrorist threats, it is not only relevant who or what an
observed object is, but also where it is now and in the past in relation to other (geographical) objects. In situation and
impact assessment, this information is used to determine whether an object is a threat. Single platform (ship, harbor) or
single sensor information will not provide all this information. The work presented in this paper focuses on the sensor
and object levels that provide a description of currently observed objects to situation assessment. For use of information
of objects at higher information levels, it is necessary to have not only a good description of observed objects at this
moment, but also from its past. Therefore, currently observed objects have to be linked to previous occurrences.
Kinematic features, as used in tracking, are of limited use, as uncertainties over longer time intervals are so large that no
unique associations can be made. Features extracted from different sensors (e.g., ESM, EO/IR) can be used for both
association and classification. Features and classifications are used to associate current objects to previous object
descriptions, allowing objects to be described better, and provide position history.
In this paper a description of a high level architecture in which such a multi-sensor association is used is described.
Results of an assessment of the usability of several features from ESM (from spectrum), EO and IR (shape, contour,
keypoints) data for association and classification are shown.