Distributed State Estimation With Two Event-Triggered Communication Strategies via Internet of Underwater Things

This article addresses a problem of event-triggered state estimation for a linear time-varying Gaussian system over the Internet of Underwater Things (IoUT), where the IoUT is a hybrid topology, including underwater acoustic wireless sensors and surface wireless network. Distributed state estimation (DSE) aims to reconstruct the system state using noisy measurements and local neighbor information, both of which are transmitted via the IoUT. However, IoUT is subject to limited energy and communication bandwidth. Each node, especially applied in underwater case, selectively transmits necessary data to maintain a minimum communication load and thus to improve energy efficiency and prolong network lifetime. To achieve this goal, we design two event-triggered strategies for typical types of wireless communication channels, of which one channel is used to transmit the local posterior information pair of filter and the other is used to transmit measurement of sensor. Then, based on the covariance intersection (CI) fusion rule and the event-triggered strategies, we develop a novel consensus-based distributed state estimator with dual event-triggered communication in a recursive form. Furthermore, after guaranteeing the network connectivity and system collective observability constraints, we derive the uniformly mean-square upper bound of the estimation error of each node. Finally, we provide an example about underwater target tracking to illustrate the effectiveness of the proposed approach.