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The Inverse Heat Mean Flow is introduced, a general-purpose solver that is compatible with a wide range of model backbones that directly learns an average velocity field through an inverse heat formul ation, thereby simplifying trajectory learning and enabling adaptive topological alignment.
The rapid proliferation of Uncrewed Aerial Vehicles (UAVs) introduces significant challenges to low-altitude airspace security, particularly from unauthorized intrusions. To address these vulnerabilities, Integrated Sensing and Communication (ISAC) has emerged as a key enabler for anti-UAV systems. However, existing studies focusing on cellular networks with fixed base stations are ill-suited for the continuous movement of target UAVs, thus failing to meet the dual demands of flexible sensing and reliable positioning. To address this, we propose an ISAC-enabled anti-UAV scheme solely based on cooperative UAVs. Specifically, we first derive the optimal transmit power under the constraint of space-air transmission outage probability tolerance. Subsequently, we deduce the sensing Fisher information matrix and Cramér-Rao Bound (CRB) by incorporating the position uncertainty of the target UAV. Then, we formulate a long-term CRB minimization problem to enhance cooperative sensing performance. To tackle this NP-hard problem, we design a robust optimization algorithm that jointly optimizes transmit-receive beamforming, association scheduling, and UAV trajectory, by transforming the structurally complex CRB matrix into a set of semi-definite constraints, and resolving the inherent position uncertainty. Numerical results demonstrate that our proposed algorithm outperforms representative algorithms in terms of sensing accuracy and robustness.
