Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) is regarded as a key enabler for next-generation wireless systems. However, conventional fixed-position antennas limit the ability of UAVs to fully exploit their inherent potential. To overcome this limitation, we propose a UAV-enabled ISAC framework equipped with fluid antennas (FAs), where the mobility of antenna elements introduces additional spatial degrees of freedom to simultaneously enhance communication and sensing performance. A multi-objective optimization problem is formulated to maximize the communication rates of multiple users while minimizing the Cramér-Rao bound (CRB) for the angle estimation of a single target. Due to excessively frequent updates of FA positions may lead to response delay, a three-timescale optimization framework is developed to jointly optimize transmit beamforming, FA positions, and UAV trajectory based on their characteristics. To solve the non-convexity of the problem, an alternating optimization-based algorithm is developed to obtain a sub-optimal solution. Numerical results show that the proposed scheme significantly outperforms various benchmark schemes, validating the effectiveness of integrating the FA technology into the UAV-enabled ISAC systems.

翻译：无人机(unmanned aerial vehicle, UAV)赋能的感知与通信一体化(integrated sensing and communication, ISAC)技术被视为下一代无线系统的重要使能技术。然而，传统固定位置天线限制了无人机充分发挥其固有潜力。为突破这一局限，本文提出一种配备流体天线(fluid antenna, FA)的无人机赋能ISAC框架，其中天线单元的移动性引入了额外空间自由度，可同时增强通信与感知性能。本文构建了一个多目标优化问题，旨在最大化多用户通信速率的同时，最小化单目标角度估计的克拉美罗界(Cramér-Rao bound, CRB)。考虑到天线位置更新过于频繁可能导致响应延迟，本文依据三种时变特性，建立了一个三时间尺度优化框架以联合优化发射波束成形、天线位置和无人机轨迹。针对问题的非凸性，本文开发了一种基于交替优化的算法获取次优解。数值结果表明，所提方案显著优于多种基准方案，验证了将流体天线技术融入无人机赋能的ISAC系统的有效性。