  The single sensor probability hypothesis density (PHD) and cardinalized
probability hypothesis density (CPHD) filters have been developed in the
literature using the random finite set framework. The existing multisensor
extensions of these filters have limitations such as sensor order dependence,
numerical instability or high computational requirements. In this paper we
derive update equations for the multisensor CPHD filter. The multisensor PHD
filter is derived as a special case. Exact implementation of the multisensor
CPHD involves sums over all partitions of the measurements from different
sensors and is thus intractable. We propose a computationally tractable
approximation which combines a greedy measurement partitioning algorithm with
the Gaussian mixture representation of the PHD. Our greedy approximation method
allows the user to control the tradeoff between computational overhead and
approximation accuracy.
