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

Despite the tremendous progress in coordinating multi-unmanned surface vehicle (USV) fleets, persistent monitoring remains a dilemma because USVs could not 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 positions in real 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, unscented Kalman filter (UKF) is employed to facilitate multi-UAV coordinated monitoring with real-time prediction and USV trajectory estimation. Effectiveness and superiority of the present method are demonstrated by both numerical simulations and real-lake based USV platform experiments.