Variable step-size estimation over UDP-based wireless networks with application to a hydrogen-powered UAV

In this paper, the problem of state estimation for systems over wireless networks using user datagram protocol is focused on. It is known that for such a system, the probability density function of the system state follows a Gaussian mixture model (GMM), and the number of components in this model grows exponentially over time, which makes the computation of optimal estimates infeasible. To compute optimal estimates, based on Kullback-Leibler divergence, a strategy with variable step-sizes to truncate and fuse the GMM is proposed. Based on the obtained GMM, a variable step-size estimator is designed to compute optimal estimates during an estimation cycle. The advantages of the proposed estimator are twofold: (1) its estimation performance is superior to that of existing one-step fast estimators; (2) its estimation efficiency is much higher than that of the optimal estimator. Finally, trajectory tracking has been proposed for a real-world hydrogen-powered unmanned aerial vehicle to show the effectiveness of our methods.