  A distributed spatio-temporal information based cooperative positioning
(STICP) algorithm is proposed for wireless networks that require
three-dimensional (3D) coordinates and operate in the global navigation
satellite system (GNSS) denied environments. Our algorithm supports any type of
ranging measurements that can determine the distance between nodes. We first
utilize a finite symmetric sampling based scaled unscented transform (SUT)
method for approximating the nonlinear terms of the messages passing on the
associated factor graph (FG) with high precision, despite relying on a small
number of samples. Then, we propose an enhanced anchor upgrading mechanism to
avoid any redundant iterations. Our simulation results and analysis show that
the proposed STICP has a lower computational complexity than the
state-of-the-art belief propagation based localizer, despite achieving an even
more competitive positioning performance.
