  This work aims to design a distributed extended object tracking (EOT) system
over a realistic network, where both the extent and kinematics are required to
retain consensus within the entire network. To this end, we resort to the
multiplicative error model (MEM) that allows the extent parameters of
perpendicular axis-symmetric objects to have individual uncertainty. To
incorporate the MEM into the information filter (IF) style, we use the
moment-matching technique to derive two pair linear models with only additive
noise. The separation is merely in a fashion, and the cross-correlation between
states is preserved as parameters in each other's model. As a result, the
closed-form expressions are transferred into an alternating iteration of two
linear IFs. With the two models, a centralized IF is proposed wherein the
measurements are converted into a summation of innovation parts. Later, under a
sensor network with the communication nodes and sensor nodes, we present two
distributed IFs through the consensus on information and consensus on
measurement schemes, respectively. Moreover, we prove the estimation errors of
the proposed filter are exponentially bounded in the mean square. The benefits
are testified by numerical experiments in comparison to state-of-the-art
filters in literature.

Systems and Control;Systems and Control