Fully Decentralized Estimation Using Square-Root Decompositions

Networks consisting of several spatially distributed sensor nodes are useful in many applications. While distributed information processing can be more robust and flexible than centralized filtering, it requires careful consideration of dependencies between local state estimates.
This paper proposes an algorithm to keep track of dependencies in decentralized systems where no dedicated fusion center is present. Specifically, it addresses double-counting of measurement information due to intermediate fusion results and correlations due to common process noise and common prior information. To limit the necessary amount of data, this paper introduces a method to partially bound correlations, leading to a more conservative fusion result than the optimal reconstruction while reducing the necessary amount of data. Simulation studies compare the performance and convergence rate of the proposed algorithm to other state-of-the-art methods.