  This paper considers the problem of tracking a large-scale number of group
targets. Usually, multi-target in most tracking scenarios are assumed to have
independent motion and are well-separated. However, for group target tracking
(GTT), the targets within groups are closely spaced and move in a coordinated
manner, the groups can split or merge, and the numbers of targets in groups may
be large, which lead to more challenging data association, filtering and
computation problems. Within the belief propagation (BP) framework, we propose
a scalable group target belief propagation (GTBP) method by jointly inferring
target existence variables, group structure, data association and target
states. The method can efficiently calculate the approximations of the marginal
posterior distributions of these variables by performing belief propagation on
the devised factor graph. As a consequence, GTBP is capable of capturing the
changes in group structure, e.g., group splitting and merging. Furthermore, we
model the evolution of targets as the co-action of the group or single-target
motions specified by the possible group structures and corresponding
probabilities. This flexible modeling enables seamless and simultaneous
tracking of multiple group targets and ungrouped targets. Particularly, GTBP
has excellent scalability and low computational complexity. It not only
maintains the same scalability as BP, i.e., scaling linearly in the number of
sensor measurements and quadratically in the number of targets, but also only
scales linearly in the number of preserved group partitions. Finally, numerical
experiments are presented to demonstrate the effectiveness and scalability of
the proposed GTBP method.
