Cooperative and distributed localization for wireless sensor networks in multipath environments

We consider the problem of sensor localization in wireless networks in a multipath environment. We propose a distributed and cooperative algorithm based on belief propagation, which allows sensors to cooperatively self-localize with respect to a single anchor node in the network, using range and direction of arrival measurements. In the algorithm, neighboring sensors exchange limited information to update their local mean location estimates and covariance matrices. We show that the covariance matrix for each sensor converges for connected networks, and its mean location estimate converges if all scatters are either parallel or orthogonal to each other. Furthermore, these estimates are asymptotically unbiased. Simulations show that cooperation amongst neighboring nodes significantly improves the localization accuracy.