  We propose a geographic and spatio-temporal information based distributed
cooperative positioning (GSTICP) algorithm for wireless networks that require
three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and
nonline-of-sight (NLOS) mixed environments. First, a factor graph (FG) is
created by factorizing the a posteriori distribution of the position-vector
estimates and mapping the spatial-domain and temporal-domain operations of
nodes onto the FG. Then, we exploit a geographic information based NLOS
identification scheme to reduce the performance degradation caused by NLOS
measurements. Furthermore, we utilize a finite symmetric sampling based scaled
unscented transform (SUT) method to approximate the nonlinear terms of the
messages passing on the FG with high precision, despite using only a small
number of samples. Finally, we propose an enhanced anchor upgrading (EAU)
mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type
of ranging measurement that can determine the distance between nodes.
Simulation results and analysis demonstrate that our GSTICP has a lower
computational complexity than the state-of-the-art belief propagation (BP)
based localizers, while achieving an even more competitive positioning
performance.
