  In this paper we derive a multi-sensor multi-Bernoulli (MS-MeMBer) filter for
multi-target tracking. Measurements from multiple sensors are employed by the
proposed filter to update a set of tracks modeled as a multi-Bernoulli random
finite set. An exact implementation of the MS-MeMBer update procedure is
computationally intractable. We propose an efficient approximate implementation
by using a greedy measurement partitioning mechanism. The proposed filter
allows for Gaussian mixture or particle filter implementations. Numerical
simulations conducted for both linear-Gaussian and non-linear models highlight
the improved accuracy of the MS-MeMBer filter and its reduced computational
load with respect to the multi-sensor cardinalized probability hypothesis
density filter and the iterated-corrector cardinality-balanced multi-Bernoulli
filter especially for low probabilities of detection.
