2023/08/10 by Mengfei Niu, Guanghui Wen, Han Shen +2 · 15 citations
Computer Science · Engineering · #Algorithm #Artificial intelligence #Computer science #Computer vision #Control theory (sociology) #Distributed Sensor Networks and Detection Algorithms #Event (particle physics) #Fault Detection and Control Systems #Filter (signal processing) #Filter design #Filtering problem #Fusion #Kalman filter #Particle filter #Position (finance) #Sensor fusion #Sequential estimation #Target Tracking and Data Fusion in Sensor Networks #Telecommunications #Tracking (education) #Transformation (genetics) #Transmission (telecommunications)
paper · doi:10.1109/taes.2023.3303859
published in IEEE Transactions on Aerospace and Electronic Systems 59(6), 8369-8379 (Institute of Electrical and Electronics Engineers)
openalex publication_date 2023/08/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/15
This article deals with the cooperative localization of maneuvering unmanned surface vessel (USV) based on multisensor fusion estimation, in which a sequential fusion filter is designed to estimate the real-time position of the USV. To avoid excessive communication consumption between sensors and the fusion filter, a stochastic event-triggered communication mechanism is adopted to ensure necessary measurements transmission. With the aid of the classical framework of sequential Bayesian filtering, an event-triggered sequential fusion filter is constructed by codesigning the stochastic event-triggered communication mechanism and the sequential filter, where a technique of unscented transformation with the sequential idea is used to resolve the intractable problem caused by nonlinear measurement models. Furthermore, a sufficient condition is established to ensure the boundedness of the fusion covariance. Finally, the effectiveness and superiority of the designed fusion filter is verified both by numerical simulation and practical experiment of a real USV tracking system.