  The aim of the present dissertation is to address distributed tracking over a
network of heterogeneous and geographically dispersed nodes (or agents) with
sensing, communication and processing capabilities. Tracking is carried out in
the Bayesian framework and its extension to a distributed context is made
possible via an information-theoretic approach to data fusion which exploits
consensus algorithms and the notion of Kullback-Leibler Average (KLA) of the
Probability Density Functions (PDFs) to be fused. The first step toward
distributed tracking considers a single moving object. Consensus takes place in
each agent for spreading information over the network so that each node can
track the object. To achieve such a goal, consensus is carried out on the local
single-object posterior distribution, which is the result of local data
processing, in the Bayesian setting, exploiting the last available measurement
about the object. The next step is in the direction of distributed estimation
of multiple moving objects. In order to model, in a rigorous and elegant way, a
possibly time-varying number of objects present in a given area of interest,
the Random Finite Set (RFS) formulation is adopted since it provides the notion
of probability density for multi-object states that allows to directly extend
existing tools in distributed estimation to multi-object tracking. The last
theoretical step of the present dissertation is toward distributed filtering
with the further requirement of unique object identities. To this end the
labeled RFS framework is adopted as it provides a tractable approach to the
multi-object Bayesian recursion. A generalization of the KLA to the labeled RFS
framework, enables the development of novel consensus multi-object tracking
filters which are fully distributed, scalable and computationally efficient.
