Cognition Difference-Based Dynamic Trust Network for Distributed Bayesian Data Fusion
Yingke Li, Ziqiao Zhang, Junkai Wang, Huibo Zhang, Enlu Zhou, Fumin Zhang
Abstract
Distributed Data Fusion (DDF), as a prevalent technique that empowers scalable, flexible, and robust information fusing, has been employed in various multi-sensor networks operating in uncertain and dynamic environments. This paper proposes a cognition difference-based mechanism to construct a dynamic trust network for real-time DDF, where the cognition difference is defined as the statistical difference between the sensors' estimated probability distributions. Distinguished by the mutual correlation between trust and cognition difference, two principles of determining trust are investigated, and their performances are analyzed by conducting simulations in the scenarios of source seeking. Our simulation and experiment results show that the proposed approach is effective in providing comprehensive and robust performance in general and unstructured environments.
BibTeX
@inproceedings{iros2023_cognitiondiffere,
title = {Cognition Difference-Based Dynamic Trust Network for Distributed Bayesian Data Fusion},
author = {Yingke Li and Ziqiao Zhang and Junkai Wang and Huibo Zhang and Enlu Zhou and Fumin Zhang},
booktitle = {IROS 2023},
year = {2023}
}