  Cooperative localization (CL) is an important technology for innovative
services such as location-aware communication networks, modern convenience, and
public safety. We consider wireless networks with mobile agents that aim to
localize themselves by performing pairwise measurements amongst agents and
exchanging their location information. Belief propagation (BP) is a
state-of-the-art Bayesian method for CL. In CL, particle-based implementations
of BP often are employed that can cope with non-linear measurement models and
state dynamics. However, particle-based BP algorithms are known to suffer from
particle degeneracy in large and dense networks of mobile agents with
high-dimensional states.
  This paper derives the messages of BP for CL by means of particle flow,
leading to the development of a distributed particle-based message-passing
algorithm which avoids particle degeneracy. Our combined particle flow-based BP
approach allows the calculation of highly accurate proposal distributions for
agent states with a minimal number of particles. It outperforms conventional
particle-based BP algorithms in terms of accuracy and runtime. Furthermore, we
compare the proposed method to a centralized particle flow-based
implementation, known as the exact Daum-Huang filter, and to sigma point BP in
terms of position accuracy, runtime, and memory requirement versus the network
size. We further contrast all methods to the theoretical performance limit
provided by the posterior Cram\'er-Rao lower bound (PCRLB). Based on three
different scenarios, we demonstrate the superiority of the proposed method.
