  This paper addresses fusion of labeled random finite set (LRFS) densities
according to the criterion of minimum information loss (MIL). The MIL criterion
amounts to minimizing the (weighted) sum of Kullback-Leibler divergences (KLDs)
with the fused density appearing as righthand argument of the KLDs. In order to
ensure the fused density to be consistent with the local ones when LRFS
densities are marginal $\delta$-generalized labeled multi-Bernoulli
(M$\delta$-GLMB) or labeled multi-Bernoulli (LMB) densities, the MIL rule is
further elaborated by imposing the constraint that the fused density be in the
same family of local ones. In order to deal with different fields-of-view
(FoVs) of the local densities, the global label space is divided into disjoint
subspaces which represent the exclusive FoVs and the common FoV of the agents,
and each local density is decomposed into the sub-densities defined in the
corresponding subspaces. Then fusion is performed subspace-by-subspace to
combine local subdensities into global ones, and the global density is obtained
by multiplying the global sub-densities. Further, in order to tackle the label
mismatching issue arising in practical applications, a rank assignment
optimization (RAO) of a suitably defined cost is carried out so as to match
labels from different agents. Moreover, issues concerning implementation of the
MIL rule and its application to distributed multitarget tracking (DMT) are
discussed. Finally, the performance of the proposed fusion approach is assessed
via simulation experiments considering DMT with either the same or different
FoVs of the agents.
