Association of labelled tracks with low reliability covariance information: a track graph partitioning approach

In the context of single air picture fusion based on tracks produced by several heterogeneous C2 system, this paper presents our original algorithmic approach and results of the decentralized fusion process. In this applicative context, the decentralized fusion node receives potentially very numerous (thousands) tracks from all registered C2. Some registered C2 are inconstant in terms of performance and accuracy, resulting in heterogeneous data and possibly in ill associations or estimations; therefore tracks and covariances are not always reliable especially in regards to consistency and velocity. A fast, parallelizable and robust track association algorithm is presented, which cope with these C2 tracks information. Track association is made by partitioning a track graph. A tactical situation maintenance strategy inspired by the state of the art in MHT (multiple hypothesis tracking) implementation is proposed.