  We propose a method for tracking an unknown number of targets based on
measurements provided by multiple sensors. Our method achieves low
computational complexity and excellent scalability by running belief
propagation on a suitably devised factor graph. A redundant formulation of data
association uncertainty and the use of "augmented target states" including
binary target indicators make it possible to exploit statistical independencies
for a drastic reduction of complexity. An increase in the number of targets,
sensors, or measurements leads to additional variable nodes in the factor graph
but not to higher dimensions of the messages. As a consequence, the complexity
of our method scales only quadratically in the number of targets, linearly in
the number of sensors, and linearly in the number of measurements per sensors.
The performance of the method compares well with that of previously proposed
methods, including methods with a less favorable scaling behavior. In
particular, our method can outperform multisensor versions of the probability
hypothesis density (PHD) filter, the cardinalized PHD filter, and the
multi-Bernoulli filter.
