Stochastic Average Consensus Filtering and Distributed State Estimation for Boolean Control Networks
arXiv:2607.28158
The paper proposes a distributed stochastic average consensus filter for Boolean control networks that enables multi‑sensor state estimation using only local communication and proves almost‑sure convergence of the algorithm.
Abstract
This paper addresses the distributed multi-sensor fusion state estimation and consensus filtering for Boolean control networks (BCNs). Existing centralized multi-sensor estimation schemes for stochastic BCNs have limitations of high communication costs and single-point failures, and continuous-state consensus algorithms are difficult to extend to discrete logical systems. By integrating probability measure transformation, semi-tensor product and stochastic approximation, a distributed stochastic average consensus filter is proposed. Moreover, the almost sure convergence of the algorithm is proved by martingale convergence theorem and perturbed stochastic Lyapunov functions. The proposed framework realizes global state estimation via local communication, avoiding the defects of centralized architectures.
25 pages, 7 figures