This paper is concerned with the problem of personnel localization in the complex coal mine environment with wireless channel fading and unknown noise statistics. Considering the random channel fading caused by signal fluctuation and transmission fault, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed. The mean and error covariances of noise are estimated adaptively by adopting the improved Sage–Husa noise estimation method. In order to save energy and improve energy utilization, the multi-sensor clustering is performed to divide the spatial distribution of sensors into multiple clusters. The sensors in the same cluster can communicate with each other to maintain the consistency of estimation. The simulation results show that the IAUKF algorithm is better than extended Kalman filter (EKF), unscented Kalman filter (UKF), and improved unscented Kalman filter (IUKF) algorithms.

No abstract

No abstract

In order to lower the drivers’ driving pressure and improve traffic efficiency, academic and industrial interest in the cooperative adaptive cruise control (CACC) system has been substantial. In this research, we use the Implicit Lyapunov Function (ILF) technique to develop a switching controller for HVPs with temporal delays. The constant time headway strategy is utilized to construct the HVPs error model first. Then, the implicit Lyapunov function is used to develop the switching controller. Finally, the conditions of internal stability and string stability are obtained. The suggested switching controller has been shown to maintain internal and string stability in simulations involving heterogeneous vehicle platoons.

This article is concerned with the set-membership state estimation problem for power distribution networks (PDNs) over a resource-constrained communication network under the influence of unknown but bounded (UBB) noises. Firstly, in order to alleviate the pressure of information communication network (ICN) while meeting the state estimation requirements, the event-triggered mechanism is adopted to send data containing more valid information to estimation center, reasonably reducing the signal transmission frequency. Secondly, an event-triggered dual set-membership filter (ET-DSMF) is designed to improve the performance of state estimation. The proposed filter performs a discrete approximation for a semi-infinite programming problem by the random sampling technique, and a compact linearization error bounding ellipsoid is obtained by solving the dual problem of the nonlinear programming. Subsequently, a sufficient condition for the existence of the estimated ellipsoid is derived depending on the mathematical induction method. The key time-varying filter gain matrix and optimal estimated ellipsoid are determined recursively by solving a convex optimization problem, according to the minimum trace criterion. Finally, the effectiveness of the proposed filtering algorithm is demonstrated by performing simulation experiments on the IEEE 13 distribution test system.