  Targets that generate multiple measurements at a given instant in time are
commonly known as extended targets. These present a challenge for many tracking
algorithms, as they violate one of the key assumptions of the standard
measurement model. In this paper, a new algorithm is proposed for tracking
multiple extended targets in clutter, that is capable of estimating the number
of targets, as well the trajectories of their states, comprising the
kinematics, measurement rates and extents. The proposed technique is based on
modelling the multi-target state as a generalised labelled multi-Bernoulli
(GLMB) random finite set (RFS), within which the extended targets are modelled
using gamma Gaussian inverse Wishart (GGIW) distributions. A cheaper variant of
the algorithm is also proposed, based on the labelled multi-Bernoulli (LMB)
filter. The proposed GLMB/LMB-based algorithms are compared with an extended
target version of the cardinalised probability hypothesis density (CPHD)
filter, and simulation results show that the (G)LMB has improved estimation and
tracking performance.
