Cooperative Group Localization Based on Factor Graph for Next-Generation Networks
Author(s) -
Xuefei Zhang,
Qimei Cui,
Yulong Shi,
Xiaofeng Tao
Publication year - 2012
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2012/438090
Subject(s) - computer science , algorithm , network topology , reliability (semiconductor) , upper and lower bounds , topology (electrical circuits) , graph , computational complexity theory , simple (philosophy) , factor (programming language) , theoretical computer science , mathematics , computer network , philosophy , physics , epistemology , quantum mechanics , combinatorics , mathematical analysis , power (physics) , programming language
In ill-conditioned communication environment, multiple target localization is of important practical significance. The cooperative group localization (CGL) model was firstly put forward, which has verified the effectiveness of localization performance gain and simultaneous multiple target localization in ill conditions. However, there exist two inherent difficulties: the strict demand for CGL topology and the high complexity. By the rational use of information to relax restrictions on topology and by dividing the complex problem into some simple local ones, the factor graph (FG) together with the sum-product algorithm is a perfect candidate for the problems above. In order to solve the two problems, we propose the weighted FG-based CGL (WFG-CGL) algorithm which incorporates the optimal weights based on the information reliability. In order to further reduce the complexity, we propose the low-complexity FG-based CGL (LCFG-CGL) algorithm. The Cramer-Rao lower bound (CRLB) of the localization error in CGL is first derived. Theoretical analysis and numerical results indicate that the proposed algorithms not only perform better in relaxing CGL topology requirement, but also enjoy high localization accuracy under low complexity in comparison with the existing CGL algorithm. © 2012 Xuefei Zhang et al.
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