A Stochastic Event-Triggered Robust Unscented Kalman Filter-Based USV Parameter Estimation

This article aims to address the remote estimation of states and model parameters for a class of unmanned surface vehicle (USVs) with unknown noise parameters and stochastic event-triggered communication mechanism. Specifically, the heavy-tailed process noises and Gaussian distributed measurement noises with unknown covariance matrices are considered. By utilizing variational Bayesian technique, a new class of online estimation approach is developed to achieve the goal of jointly estimating the states, USV model parameters, and noise parameters in a remote manner. Due to the inherent nonlinearity of the augmented system, the unscented transformation is incorporated into the estimator design. In addition, to balance the tradeoff between estimation effectiveness and communication rate, the objective of joint estimation is realized under the event-triggered mechanism with the help of Gaussianity. Finally, the performance of the proposed event-triggered robust unscented Kalman filter is demonstrated by practical experiments as well as numerical simulations.