Abstract
A number of applications in wireless sensor networks (WSN) require sensor nodes to obtain their absolute or relative positions. This chapter applies probabilistic inference to the problem of cooperative localization. Belief propagation (BP) is a way of organizing the global computation of marginal beliefs in terms of smaller local computations within the graph. However, due to the presence of nonlinear relationships and highly non‐Gaussian uncertainties, the standard BP algorithm is undesirable. Nevertheless, a particle‐based approximation via nonparametric belief propagation (NBP) makes BP acceptable for localization in sensor networks. The chapter describes BP/NBP techniques and Gaussian BP (GBP) for the loopy networks. Due to the poor performance of BP/NBP methods in loopy networks, it describes three improved methods: GBP based on Kikuchi approximation (GBP‐K), nonparametric GBP based on junction tree (NGBP‐JT), NBP based on spanning trees (NBP‐ST), and uniformly‐reweighted NBP (URW‐NBP).
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