This article proposes a Koopman-enhanced distributed filtering for unknown stochastic nonlinear systems using contaminated datasets. To overcome the limitation that conventional Koopman operators fail to handle unknown stochastic dynamics, a delay-coordinate embedding strategy is introduced to reconstruct the lifting observations from noisy measurements. Moreover, to suppress the adverse effects of process and measurement noise on the invariant subspace, a robust subspace dynamic mode decomposition (SDMD) method is developed for reliable Koopman operator identification. Within this framework, a distributed filtering scheme is designed that exploits both direct and indirect measurements, where an adaptive event-triggered mechanism is further derived to balance network transmission burden and estimation accuracy. Finally, simulation results demonstrate the effectiveness and robustness of the proposed filtering approach.
Zheng et al. (Thu,) studied this question.