Abstract: Integrated Sensing and Communication (ISAC) has been recognized as one of the key enabling technologies for future wireless networks. Reconfigurable Intelligent Surface (RIS)-assisted multi-Unmanned Aerial Vehicle (UAV) cooperative ISAC systems can simultaneously exploit the advantages of wireless propagation environment reconfiguration and aerial mobility. However, such systems face significant challenges, including strong coupling among optimization variables, high-dimensional decision spaces, and the difficulty of jointly enhancing communication and sensing performance. To address these issues, this paper proposes a joint optimization framework for RIS-assisted multi-UAV cooperative ISAC systems. A three-dimensional discrete-time system model consisting of multiple UAVs, multiple ground base stations, a sensing target, and an RIS is established, where UAV mobility constraints, RIS reflection mechanisms, air-to-ground communication processes, and target sensing processes are jointly characterized. A non-convex optimization problem is formulated to maximize a weighted utility comprising the system sum communication rate and a sensing performance metric based on the log-determinant of a dimensionless Fisher Information Matrix (FIM), with transmit beamforming, UAV trajectories, and RIS phase shifts jointly optimized. The sensing utility is derived from the cumulative FIM contributions of both direct sensing links and RIS-assisted sensing links, which explicitly captures the impacts of UAV spatial deployment, RIS configuration, and target observation geometry on sensing performance. To tackle the non-convex objective function, unit-modulus constraints, and the strong coupling between communication and sensing components, an Alternating Optimization (AO) framework is developed. Specifically, the original problem is decomposed into three subproblems: transmit beamforming optimization, UAV trajectory optimization, and RIS phase-shift optimization. For transmit beamforming optimization, the Weighted Minimum Mean Square Error (WMMSE) method is employed to transform the weighted sum-rate maximization problem into an equivalent iterative minimization problem, while incorporating sensing-aware weighting for resource allocation. For UAV trajectory optimization, the Successive Convex Approximation (SCA) method is adopted to locally convexify the communication rate term, sensing utility term, and safety-distance constraints, and trust-region and smoothing regularization techniques are introduced to improve trajectory feasibility and numerical stability. For RIS phase-shift optimization, the non-convex quadratically constrained quadratic programming problem under unit-modulus constraints is transformed into a Semidefinite Relaxation (SDR) problem, and a grouping strategy combined with Gaussian randomization is employed to reduce the complexity of high-dimensional RIS phase recovery. Simulation results demonstrate that the proposed algorithm exhibits stable convergence behavior under different system parameters and network geometries, typically reaching convergence within 8–15 iterations. Statistical results obtained from 100 independent Monte Carlo trials show that, compared with the non-RIS scheme, the proposed RIS-assisted joint optimization method improves the average system communication rate by 24.83%, indicating that RIS-enabled propagation environment reconfiguration can effectively enhance air-to-ground communication link quality. Compared with the communication-only optimization scheme, incorporating the FIM-based sensing utility increases the average flight altitude of multiple UAVs by 16.58%, demonstrating that the proposed joint objective can guide UAVs to form more favorable spatial observation geometries for target localization while simultaneously satisfying multi-UAV safety constraints. Furthermore, sensitivity analysis with respect to communication-sensing weighting factors and experiments involving different numbers of UAVs reveal a pronounced synergistic effect among RIS-enabled environment reconfiguration, multi-UAV spatial mobility, and FIM-based sensing information modeling. Overall, the proposed method achieves communication rate enhancement, sensing geometry improvement, and comprehensive utility maximization while satisfying UAV safety-flight constraints and RIS unit-modulus constraints, providing an effective solution for joint resource allocation and trajectory design in complex air-ground ISAC networks.

摘要： 通感一体化(Integrated Sensing and Communication，ISAC)被认为是未来无线网络的关键技术之一。可重构智能表面(Reconfigurable Intelligent Surface，RIS)辅助的多无人机(Unmanned Aerial Vehicle，UAV)协同ISAC系统能够同时发挥传播环境调控与空域机动优势，但也面临变量耦合强、优化维度高和通信—感知性能难以协同提升等问题。为此，提出一种RIS辅助多UAV协同ISAC系统联合优化方法。构建由多架UAV、多个地面基站、单个感知目标和RIS组成的三维离散时隙系统模型，统一刻画UAV运动约束、RIS反射机制、空地通信过程与目标感知过程；以系统总通信速率和基于无量纲化费舍尔信息矩阵(Fisher Information Matrix，FIM)对数行列式的感知性能指标为联合目标，建立关于发射波束、UAV轨迹和RIS相移的非凸优化问题。其中，FIM感知项由直接链路和RIS辅助链路对目标位置参数的信息贡献累加得到，可显式反映UAV空间部署、RIS位置和目标观测几何结构对感知性能的影响。针对该问题目标函数非凸、单位模约束难处理、通信干扰项和感知项高度耦合等特点，设计交替优化(Alternating Optimization，AO)求解框架，将原问题分解为发射波束优化、UAV轨迹优化和RIS相移优化三个子问题。在发射波束优化中，利用加权最小均方误差(Weighted Minimum Mean Square Error, WMMSE)方法将加权和速率最大化问题转化为等价的迭代最小化问题，并结合感知权重引导波束资源分配；在UAV轨迹优化中，采用逐次凸近似(Successive Convex Approximation，SCA)方法对通信速率项、感知效用项和安全距离约束进行局部凸化，并引入信赖域和平滑正则项提高轨迹可执行性和数值稳定性；在RIS相移优化中，将单位模约束下的非凸二次规划问题转化为半定松弛(Semidefinite Relaxation, SDR)问题，并结合分组策略和高斯随机化降低高维RIS相位恢复复杂度。仿真结果表明，所提算法在不同系统参数和不同网络几何部署下均表现出稳定的收敛特性，通常在8～15次迭代内达到稳定状态。基于100次独立蒙特卡罗试验的统计结果显示，与无RIS方案相比，所提RIS辅助联合优化方法可使系统平均通信速率提升24.83%，说明RIS的传播环境调控能力能够有效增强空地通信链路质量；与仅通信优化方案相比，引入FIM感知项后，多UAV平均飞行高度提升16.58%，说明联合目标能够引导UAV形成更有利于目标定位的空间观测结构，能够在满足多UAV安全飞行约束的同时实现通信性能与感知性能的协同优化；权重敏感性实验与UAV数量影响实验进一步表明，RIS的传播环境调控能力、多UAV的空间机动能力以及FIM感知信息量建模之间具有明显协同作用。综上可得，所提方法能够在满足UAV安全飞行约束和RIS单位模约束的条件下，实现通信速率提升、感知几何结构改善和系统综合效用增强，可为复杂空地ISAC网络中的联合资源配置与轨迹设计提供有效方案。