Distributed maximum correntropy unscented Kalman filtering with state equality constraints

This article investigates the distributed maximum correntropy unscented Kalman filtering problem for nonlinear systems via a sensor network. The system dynamics is subject to state equality constraints and non-Gaussian noise. By utilizing the maximum correntropy criterion to handle non-Gaussian noise, a centralized maximum correntropy constrained unscented Kalman filter is first proposed. Then, two novel distributed maximum correntropy constrained unscented Kalman filters with special features are designed. Specifically, the first one is developed by approximating the centralized filter with each sensor's own and its neighbors' measurements. The other one is designed by fusing state estimates. It is worth mentioning that these two distributed algorithms only need finite steps to fuse information over the sensor network rather than infinite steps to achieve the average consensus. Finally, the validity of the proposed algorithms is demonstrated by simulation experiments, with a detailed comparison.