Multi-sensor multi-target tracking with robust kinematic data based credal classification
Samir Hachour, François Delmotte, David Mercier, Éric Lefèvre · 2013
Multi-target tracking using multiple sensors is an important research field in application areas of mobile systems and military applications. This paper proposes a decentralized multi-sensor, multi-target tracking and belief (credal) based classification approach, applied to maritime targets. A given number of sensors, considered as unreliable, are designed to locally predict and update targets states using Interacting Multiple Model (IMM) algorithms (one IMM for one target). Targets IMMs are updated by sequentially acquired measurements. The measurements are assigned to the targets by the means of a generalized Global Nearest Neighbor (GNN) algorithm. The generalized GNN algorithm is able to provide information on the newly detected or non-detected targets and these information is used by score functions which manage the targets appearances and disappearances. In addition to the tracking task of multiple targets, each sensor performs a local classification of each one of the targets. The unreliability of the sensors makes the local classifications weak. In this article, a global classification method is shown to improve the sensors classification performances.