  This paper proposes an efficient implementation of the multi-sensor
generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the
GLMB joint prediction and update together with a new technique for truncating
the GLMB filtering density based on Gibbs sampling. The resulting algorithm has
quadratic complexity in the number of hypothesized object and linear in the
number of measurements of each individual sensors.
