  This paper addresses the density based multi-sensor cooperative fusion using
random finite set (RFS) type multi-object densities (MODs). Existing fusion
methods use scalar weights to characterize the relative information confidence
among the local MODs, and in this way the portion of contribution of each local
MOD to the fused global MOD can be tuned via adjusting these weights. Our
analysis shows that the fusion mechanism of using a scalar coefficient can be
oversimplified for practical scenarios, as the information confidence of an MOD
is complex and usually space-varying due to the imperfection of sensor ability
and the various impacts from surveillance environment. Consequently, severe
fusion performance degradation can be observed when these scalar weights fail
to reflect the actual situation. We make two contributions towards addressing
this problem. Firstly, we propose a novel heterogeneous fusion method to
perform the information averaging among local RFS MODs. By factorizing each
local MODs into a number of smaller size sub-MODs, it can transform the
original complicated fusion problem into a much easier parallelizable
multi-cluster fusion problem. Secondly, as the proposed fusion strategy is a
general procedure without any particular model assumptions, we further derive
the detailed heterogeneous fusion equations, with centralized network
architecture, for both the probability hypothesis density (PHD) filter and the
multi-Bernoulli (MB) filter. The Gaussian mixture implementations of the
proposed fusion algorithms are also presented. Various numerical experiments
are designed to demonstrate the efficacy of the proposed fusion methods.
