Event-based Multisensor Fusion with Correlated Estimates

Many automation tasks require to fuse information that is acquired by distributed sensors and passed through a wireless network across multiple nodes. The growing number of connected sensors and agents increases the burden on the communications network and the energy consumption. Further challenges in information fusion arise from correlated data shared between nodes. To mitigate the negative effects, an efficient multi-sensor fusion approach is presented in this paper. A system design that uses stochastic event-based instead of periodic transmissions is proposed based on two different algorithms, the augmented state approach and fast covariance intersection. Furthermore, two different network topologies are investigated and a methodology to handle correlations among both finite impulse response and recursive estimates is developed. Together, the results represent a wide range of network topologies and possible correlation structures and give insights into the estimation performance and network utilization.