  The growing need for accurate and reliable tracking systems has driven
significant progress in sensor fusion and object tracking techniques. In this
paper, we design two variational Bayesian trackers that effectively track
multiple targets in cluttered environments within a sensor network. We first
present a centralised sensor fusion scheme, which involves transmitting sensor
data to a fusion center. Then, we develop a distributed version leveraging the
average consensus algorithm, which is theoretically equivalent to the
centralised sensor fusion tracker and requires only local message passing with
neighbouring sensors. In addition, we empirically verify that our proposed
distributed variational tracker performs on par with the centralised version
with equal tracking accuracy. Simulation results show that our distributed
multi-target tracker outperforms the suboptimal distributed sensor fusion
strategy that fuses each sensor's posterior based on arithmetic sensor fusion
and an average consensus strategy.
