Sensing-Assisted Predictive Beamforming for UAV-Enabled Ocean Monitoring Networks

This paper investigates a sensing-assisted predictive beamforming framework for UAV--buoy maritime monitoring by explicitly accounting for wave-induced buoy dynamics and residual sea clutter. A frame-based UAV mission workflow is first established, where the UAV transmits integrated sensing and communication signals to acquire buoy echoes and to support subsequent uplink beam alignment. To characterize short-horizon buoy motion, a correlated-acceleration state-space model is developed by combining a Singer process for wave-driven excitation with a slowly varying current-drift term. Given the resulting nonlinear reflection, Doppler, and delay measurements, the posterior Fisher information matrix and the corresponding posterior Cramér--Rao bound (PCRB) are derived, and the predicted horizontal-position PCRB is adopted as the sensing metric. A per-frame worst-buoy design is then formulated to jointly optimize sensing power allocation and UAV position under uplink-rate, UAV-power, and mobility constraints. By exploiting a Schur-complement reformulation and a lagged successive convex approximation, the resulting subproblem is converted into a convex conic program with tractable complexity. Simulation results show that the proposed scheme maintains robust prediction and communication performance under denser buoy deployments and harsher sea conditions, and outperforms several baseline designs. In particular, the pronounced root mean square error (RMSE) degradation of the communication-only benchmark confirms that sensing-assisted state refinement is essential for accurate predictive beamforming in dynamic maritime environments. Compared with a full first-order Taylor expansion method, it achieves a more attractive performance--complexity tradeoff for online deployment.