Neural Enhanced Belief Propagation for Cooperative Localization
Mingchao Liang, Florian Meyer

TL;DR
This paper introduces a hybrid approach combining belief propagation and graph neural networks to improve the accuracy and consistency of cooperative localization in wireless networks, addressing overconfidence issues.
Contribution
It proposes a novel neural-enhanced belief propagation method that integrates data-driven GNNs with model-based BP for better localization accuracy and belief consistency.
Findings
Improved localization accuracy over traditional BP.
Reduced overconfidence in estimated beliefs.
Computational complexity remains comparable to BP.
Abstract
Location-aware networks will introduce innovative services and applications for modern convenience, applied ocean sciences, and public safety. In this paper, we establish a hybrid method for model-based and data-driven inference. We consider a cooperative localization (CL) scenario where the mobile agents in a wireless network aim to localize themselves by performing pairwise observations with other agents and by exchanging location information. A traditional method for distributed CL in large agent networks is belief propagation (BP) which is completely model-based and is known to suffer from providing inconsistent (overconfident) estimates. The proposed approach addresses these limitations by complementing BP with learned information provided by a graph neural network (GNN). We demonstrate numerically that our method can improve estimation accuracy and avoid overconfident beliefs,…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Distributed Sensor Networks and Detection Algorithms · Target Tracking and Data Fusion in Sensor Networks
MethodsGraph Neural Network
