Transmission Censoring and Information Fusion for Communication-Efficient Distributed Nonlinear Filtering

A transmission censoring and information fusion approach is proposed for distributed nonlinear system state estimation in Dynamic Data Driven Applications Systems (DDDAS). In this approach, to conserve communication resources, based on the Jeffreys divergence between the prior and posterior probability density functions (PDFs) of the system state, only local posterior PDFs that are sufficiently different from their corresponding prior PDFs will be transmitted to a fusion center. To further reduce the communication cost, the local posterior PDFs are approximated by Gaussian mixtures, whose parameters are learned by an expectation-maximization algorithm. At the fusion center, the received PDFs will be fused via a generalized covariance intersection algorithm to obtain a global PDF. Numerical results for a multi-senor radar target tracking example are provided to demonstrate the effectiveness of the proposed censoring approach.