On Generalized Bayesian Data Fusion with Complex Models in Large Scale Networks

TL;DR

This paper develops advanced Bayesian data fusion algorithms for complex models in large-scale sensor networks, addressing challenges of dynamic topologies and complex belief representations to improve distributed reasoning.

Contribution

It introduces novel generalized DDF algorithms capable of handling complex probabilistic beliefs like Gaussian mixtures and hybrid networks, with mathematical insights and practical algorithms.

Findings

Enhanced fusion accuracy in multi-robot target search scenarios

Significant improvements over existing DDF methods

Potential for robust distributed intelligent reasoning

Abstract

Recent advances in communications, mobile computing, and artificial intelligence have greatly expanded the application space of intelligent distributed sensor networks. This in turn motivates the development of generalized Bayesian decentralized data fusion (DDF) algorithms for robust and efficient information sharing among autonomous agents using probabilistic belief models. However, DDF is significantly challenging to implement for general real-world applications requiring the use of dynamic/ad hoc network topologies and complex belief models, such as Gaussian mixtures or hybrid Bayesian networks. To tackle these issues, we first discuss some new key mathematical insights about exact DDF and conservative approximations to DDF. These insights are then used to develop novel generalized DDF algorithms for complex beliefs based on mixture pdfs and conditional factors. Numerical examples motivated by multi-robot target search demonstrate that our methods lead to significantly better fusion results, and thus have great potential to enhance distributed intelligent reasoning in sensor networks.