Proceedings Article | 23 May 2011
KEYWORDS: Sensors, Data fusion, Radar, Artificial intelligence, Target detection, Electronic filtering, Satellites, Maritime surveillance, Mathematical modeling, Kinematics
Multi sensor fusion techniques are widely employed in several surveillance applications (e.g., battlefield monitoring, air
traffic control, camp protection, etc). The necessity of tracking the elements of a dynamic system usually requires
combining information from heterogeneous data sources in order to overcome the limitations of each sensor. The
gathered information might be related to the target kinematics (position, velocity), its physical features (shape, size,
composition) or intentions (route plan, friend/foe, engaged sensor modes, etc). The combination of such heterogeneous
sensor data proved to benefit from the exploitation of context information, i.e., static and dynamic features of the
scenario, represented in a Knowledge Base (KB). A Geographic Information System (GIS) is a typical example for a KB
that can be exploited for the enhancement of multi sensor data fusion.
The present paper describes potential strategies for "knowledge-based" data fusion in the area of Maritime Situational
Awareness (MSA). MSA is founded on the data from heterogeneous sources, including radars, Navigation Aids, air- and
space-based monitoring services, and recently-conceived passive sensors. Several strategies for optimally fusing two or
more of these information data flows have been proposed for MSA applications. Relevant KB information comprises
port locations, coastal lines, preferred routes, traffic rules, and potentially a maritime vessel database. We propose
mathematical models and techniques to integrate kinematic constraints, e.g., in terms of navigation fields, and different
object behaviour into a data fusion approach. For an exemplary sensor suite, we evaluate performance measures in the
framework of centralised and decentralised fusion architectures.
