Scalable decentralised data fusion using hypercube gossiping

We present a scalable approach to distributed data fusion based on the bandwidth- and latency-efficient hypercube topology. Provided the individual estimation errors are uncorrelated, double-counting is avoided altogether by exploitation of the network topology, without the need for approximation with channel filters or pedigree logs. The fully allocated network is symmetric and exhibits no special nodes or network locations. Our approach uses simple O (log n) systolic information dissemination without the need to first determine an ad-hoc communication scheme. As a secondary contribution we present practical considerations on how to reduce bandwidth for severely limited bandwidth scenarios and present an optimised timing mode that reduces mean information latency.