RESEARCH ARTICLE

Real-time cross-domain monitoring of multi-UAV-multi-USV systems via efficient block sparse Bayesian learning

¹ School of Artificial Intelligence and Automation, Institute of Artificial Intelligence, Engineering Research Center of Autonomous Intelligent Unmanned Systems (Ministry of Education), Huazhong University of Science and Technology, Wuhan 430074, China
² Guangdong HUST Industrial Technology Research Institute, Huazhong University of Science and Technology, Dongguan 523808, China
³ School of Artificial Intelligence, Optics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an 710072, China

^* Corresponding author (email: This email address is being protected from spambots. You need JavaScript enabled to view it. )

Received: 14 September 2025
Revised: 27 November 2025
Accepted: 5 December 2025

Abstract

Despite the tremendous progress in coordinating multi-unmanned surface vehicle (USV) fleets, persistent monitoring remains a dilemma because USVs cannot share data with external monitors. Practical deployments further impose real-time constraints and limited onboard calculation capability, necessitating low-complexity algorithms. This study proposes a multi-UAV fleet-based monitoring scheme. Therein, UAVs are assigned to pairwise USV-UAV matching to observe relative positionreal time. An efficient block sparse Bayesian learning algorithm (EBSBL) is then developed to identify the coordinated dynamics of USVs, with theoretically guaranteed feasibility. In addition, the unscented Kalman filter (UKF) is employed to facilitate multi-UAV coordinated monitoring with real-time prediction and USV trajectory estimation. The effectiveness and superiority of the proposed method are demonstrated by both numerical simulations and real-lake based multi-UAV-multi-USV platform experiments.