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Gaussian Message Passing-Based Cooperative Localisation with Bootstrap Percolation Scheme in Dense Networks

Published online by Cambridge University Press:  02 May 2019

Yangyang Liu
Affiliation:
(School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China)
Baowang Lian
Affiliation:
(School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China)
Lin Zhang
Affiliation:
(School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China)
Taoyun Zhou
Affiliation:
(School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China)
Corresponding
E-mail address:

Abstract

In dense networks, the tremendous computational complexity and communication overhead of cooperative localisation are the two main bottlenecks that limit practical application. In this study, we introduce a bootstrap percolation scheme into Gaussian message passing-based cooperative localisation for precise positioning, aimed at reducing the system overhead. Considering the uncertainty information and geometric distribution of neighbours, an approximate collinear detection criterion is proposed to detect the possible flip ambiguities in cooperative localisation. According to the detection result and our connection constraint, agents are divided into three categories and are approximated by different distribution families. A message passing rule is designed to control the propagation direction from high precision to low precision, thereby mitigating potential error propagation. Additionally, a layer-by-layer positioning mechanism is established where the agents are located gradually. Analytical and simulation results indicate that when the ranging standard deviation is 0·2 m, 89·3% of the agent nodes can be located within 0·4 m using the proposed algorithm. Compared with the Hybrid Sum-Product Algorithm over A Wireless Network (H-SPAWN), this ratio is increased by 8·4% and the computational complexity is reduced by 72%.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2019 

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