Distributed Minimum Error Entropy Kalman Filtering for Networked Systems with Packet Losses

This paper addresses the issues of potential packet losses during communication and non-Gaussian noise interference in multi-sensor networked systems. Initially, the Cauchy kernel function-based minimum error entropy criterion is employed to measure the similarity of the information entropy of state estimation errors, thereby mitigating outliers caused by non-Gaussian noise. Subsequently, a set of independent and identically distributed Bernoulli random variables is utilized to characterize the phenomena of packet losses occurring during data communication between sensor nodes. What's more, the system model is reconstructed using an augmented coefficient matrix that incorporates random variables. Building upon this foundation, a distributed minimum error entropy fusion filtering algorithm with a fixed-point iterative form is derived. Finally, the effectiveness of the proposed filtering algorithm is validated through numerical simulations.