Belief propagation based AIS/radar data fusion for multi-target tracking

Abstract

A data fusion technique aiming at combining observations from two classes of sensors is proposed. The first class consists of sensors that produce periodic noisy observations of the targets; moreover, they may also miss the targets or generate false alarms. Sensors belonging to the second class, instead, do not generate false alarms, and provide aperiodic noisy observations of the targets that may have an identity. The problem is formalised with specific application to the maritime domain, in which radar sensors and the Automatic Identification System (AIS) are selected as representatives of the two classes, respectively. A Bayesian framework is developed and a detection-estimation problem is formulated, which is then efficiently solved with the use of a Belief Propagation (BP) message passing scheme. The performance and the effectiveness of the proposed algorithm is evaluated in a simulated scenario.