OPTIMIZED EDGE APPEARANCE PROBABILITY FOR COOPERATIVE LOCALIZATION BASED ON TREE-REWEIGHTED NONPARAMETRIC BELIEF PROPAGATION

Distributed and Cooperative Processing

Přednášející: Vladimir Savic, Autoři: Vladimir Savic, Universidad Politecnica de Madrid, Spain; Henk Wymeersch, Chalmers University of Technology, Sweden; Federico Penna, Politecnico di Torino, Italy; Santiago Zazo, Universidad Politecnica de Madrid, Spain

Nonparametric belief propagation (NBP) is a well-known particle-based method for distributed inference in wireless networks. NBP has a large number of applications, including cooperative localization. However, in loopy networks NBP suffers from similar problems as standard BP, such as over-confident beliefs and possible non-convergence. Tree-reweighted NBP (TRW-NBP) can mitigate these problems, but does not easily lead to a distributed implementation due to the non-local nature of the required so-called edge appearance probabilities. In this paper, we propose a variation of TRW-NBP, suitable for cooperative localization in wireless networks. Our algorithm uses a fixed edge appearance probability for every edge, and can outperform standard NBP in dense wireless networks.

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