  This paper, the fourth part of a series of papers on the arithmetic average
(AA) density fusion approach and its application for target tracking, addresses
the intricate challenge of distributed heterogeneous multisensor multitarget
tracking, where each inter-connected sensor operates a probability hypothesis
density (PHD) filter, a multiple Bernoulli (MB) filter or a labeled MB (LMB)
filter and they cooperate with each other via information fusion. Earlier
papers in this series have proven that the proper AA fusion of these filters is
all exactly built on averaging their respective unlabeled/labeled PHDs. Based
on this finding, two PHD-AA fusion approaches are proposed via variational
minimization of the upper bound of the Kullback-Leibler divergence between the
local and multi-filter averaged PHDs subject to cardinality consensus based on
the Gaussian mixture implementation, enabling heterogeneous filter cooperation.
One focuses solely on fitting the weights of the local Gaussian components
(L-GCs), while the other simultaneously fits all the parameters of the L-GCs at
each sensor, both seeking average consensus on the unlabeled PHD, irrespective
of the specific posterior form of the local filters. For the distributed
peer-to-peer communication, both the classic consensus and flooding paradigms
have been investigated. Simulations have demonstrated the effectiveness and
flexibility of the proposed approaches in both homogeneous and heterogeneous
scenarios.
