  We address the problem of distributed cooperative localization in wireless
networks, i.e. nodes without prior position knowledge (agents) wish to
determine their own positions. In non-cooperative approaches, positioning is
only based on information from reference nodes with known positions (anchors).
However, in cooperative positioning, information from other agents is
considered as well. Cooperative positioning requires encoding of the
uncertainty of agents' positions. To cope with that demand, we employ
stochastic inference for localization which inherently considers the position
uncertainty of agents. However, stochastic inference comes at the expense of
high costs in terms of computation and information exchange. To relax the
requirements of inference algorithms, we propose the framework of
position-constrained stochastic inference, in which we first confine the
positions of nodes to feasible sets. We use convex polygons to impose
constraints on the possible positions of agents. By doing so, we enable
inference algorithms to concentrate on important regions of the sample space
rather than the entire sample space. We show through simulations that increased
localization accuracy, reduced computational complexity, and quicker
convergence can be achieved when compared to a state-of-the-art non-constrained
inference algorithm.
