Full order nonlinear distributed estimation in intermittently connected networks

The paper considers the problem of performing distributed particle filtering in intermittently connected networks with nonlinear state dynamics. In the context of large, geographically-distributed sensor networks, communication delays affect the convergence of the consensus algorithms used to derive the global state estimate from local estimates. We propose a non-linear fusion rule that relaxes the condition of requiring convergence of the consensus step between two successive iterations of the localized particle filters, thereby, allowing the consensus step to catch up with the localized filters in case of communication delays. Our Monte Carlo simulations illustrate the ability of the modified consensus/fusion based distributed implementation of the particle filter (MCF/DPF) to successfully handle intermittence in the network connectivity.