  This paper addresses the problem of multitarget tracking using a network of
sensing agents with unknown positions. Agents have to both localize themselves
in the sensor network and, at the same time, perform multitarget tracking in
the presence of clutter and miss detection. These two problems are jointly
resolved using a holistic and centralized approach where graph theory is used
to describe the statistical relationships among agent states, target states,
and observations. A scalable message passing scheme, based on the sum-product
algorithm, enables to efficiently approximate the marginal posterior
distributions of both agent and target states. The proposed method is general
enough to accommodate a full multistatic network configuration, with multiple
transmitters and receivers. Numerical simulations show superior performance of
the proposed joint approach with respect to the case in which cooperative
self-localization and multitarget tracking are performed separately, as the
former manages to extract valuable information from targets. Lastly, data
acquired in 2018 by the NATO Science and Technology Organization (STO) Centre
for Maritime Research and Experimentation (CMRE) through a network of
autonomous underwater vehicles demonstrates the effectiveness of the approach
in a practical application.
