  We propose a distributed cooperative positioning algorithm using the extended
Kalman filter (EKF) based spatio-temporal data fusion (STDF) for a wireless
network composed of sparsely distributed high-mobility nodes. Our algorithm
first makes a coarse estimation of the position and mobility state of the nodes
by using the prediction step of EKF. Then it utilizes the coarse estimate as
the prior of STDF that relies on factor graph (FG), thus facilitates inferring
a posteriori distributions of the agents' positions in a distributed manner. We
approximate the nonlinear terms of the messages passed on the associated FG
with high precision by exploiting the second-order Taylor polynomial and obtain
closed-form representations of each message in the data fusion step, where
temporal measurements by imperfect hardware are considered additionally. In the
third stage, refinement of position estimate is performed by invoking the
update step of EKF. Simulation results and analysis show that our EKF-STDF has
a lower computational complexity than the state-of-the-art EKF-based
algorithms, while achieving an even superior positioning performance in harsh
environment.
