2013 IEEE Global Conference on Signal and Information Processing 2013
DOI: 10.1109/globalsip.2013.6736945
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Mean field message passing for cooperative simultaneous ranging and synchronization

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Cited by 5 publications
(10 citation statements)
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“…1. Inserting (9) and (11) into (13) allows each vertex p(yn|xn) to be represented by a subgraph as given in [14], and each vertex p(xn|xn−1) by node-independent transitions. For each state xn, we can thus compute the marginal function m n|n (x k,n ) ∝ Nx k,n (µ k,n , Σ k,n ) locally for each state entry x k,n [xn] k , which corresponds to the MMSE estimate of either clock parameters ϑi,n or a distance dij,n.…”
Section: Distributed Filteringmentioning
confidence: 99%
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“…1. Inserting (9) and (11) into (13) allows each vertex p(yn|xn) to be represented by a subgraph as given in [14], and each vertex p(xn|xn−1) by node-independent transitions. For each state xn, we can thus compute the marginal function m n|n (x k,n ) ∝ Nx k,n (µ k,n , Σ k,n ) locally for each state entry x k,n [xn] k , which corresponds to the MMSE estimate of either clock parameters ϑi,n or a distance dij,n.…”
Section: Distributed Filteringmentioning
confidence: 99%
“…For each state xn, we can thus compute the marginal function m n|n (x k,n ) ∝ Nx k,n (µ k,n , Σ k,n ) locally for each state entry x k,n [xn] k , which corresponds to the MMSE estimate of either clock parameters ϑi,n or a distance dij,n. The resulting FG for BP can be represented graphically by stacking the FG for the posterior function of [14] in discrete-time n and additionally introducing transition functions p(x k,n |x k,n−1 ) for each vertex x k,n . Similar to the discussion in [14], disjoint subgraphs of this FG can be related to physical network nodes, which leads to a distributed algorithm.…”
Section: Distributed Filteringmentioning
confidence: 99%
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