Decentralized Bayesian consensus over networks

This paper deals with networked, dynamical multi-agent systems (MAS) trying to reach consensus about their states subject to uncertain data transfer and noisy measurements. For this, an analogy between the deterministic consensus protocol and a Gaussian process is established. First, the consensus problem is modeled as a stochastic process to consider uncertain initial states and noisy information flow over the network. Next, necessary conditions for decentral inference are derived, two decentral approximative inference protocols are developed and the dependency between communication density and approximation error is presented. Furthermore, a provably convergent and computationally efficient Gaussian consensus protocol is realized. Finally, it is shown that taking measurement noise into account the Gaussian consensus protocol naturally extends to a decentralized Kalman filter for consensus systems.