  This paper addresses distributed registration of a sensor network for
multitarget tracking. Each sensor gets measurements of the target position in a
local coordinate frame, having no knowledge about the relative positions
(referred to as drift parameters) and azimuths (referred to as orientation
parameters) of its neighboring nodes. The multitarget set is modeled as an
independent and identically distributed (i.i.d.) cluster random finite set
(RFS), and a consensus cardinality probability hypothesis density (CPHD) filter
is run over the network to recursively compute in each node the posterior RFS
density. Then a suitable cost function, xpressing the discrepancy between the
local posteriors in terms of averaged Kullback-Leibler divergence, is minimized
with respect to the drift and orientation parameters for sensor registration
purposes. In this way, a computationally feasible optimization approach for
joint sensor registraton and multitarget tracking is devised. Finally, the
effectiveness of the proposed approach is demonstrated through simulation
experiments on both tree networks and networks with cycles, as well as with
both linear and nonlinear sensors.
