This paper investigates energy efficiency maximization in an integrated sensing and communication framework for satellite-UAV MIMO systems, where a LEO satellite and a UAV simultaneously serve ground users and perform target sensing. Both the satellite and UAV are equipped with uniform planar arrays of transmit antennas, enabling a distributed multi-user and multi-target architecture. We derive the achievable downlink throughput by considering that the high-altitude satellite maintains a line-of-sight (LoS) link with users, while adopting a probabilistic model for the UAV that accounts for the likelihood of both LoS and non-line-of-sight conditions. The energy efficiency maximization problem is formulated as a complex non-convex optimization problem, subject to power constraints, quality of service (QoS) requirements, and beampattern gain constraints for accurate sensing. To tackle this challenge, we propose an efficient alternating optimization algorithm capable of handling the complex search space and QoS guarantees. Numerical results across diverse scenarios with multiple users demonstrate that the proposed method achieves high energy efficiency while meeting both communication and sensing performance targets.

翻译：本文研究了卫星-无人机多输入多输出系统中集成感知与通信框架下的能效最大化问题，其中低轨卫星与无人机协同为地面用户提供服务并执行目标感知任务。卫星与无人机均配备均匀平面发射天线阵列，构成分布式多用户多目标架构。通过考虑高空卫星与用户间保持视距链路，并对无人机采用同时涵盖视距与非视距条件的概率模型，推导了可达下行链路吞吐量。能效最大化问题被构建为一个复杂的非凸优化问题，其约束包括功率限制、服务质量要求以及用于精确感知的波束赋形增益约束。为应对这一挑战，本文提出一种高效的交替优化算法，能够处理复杂的搜索空间并保障服务质量。在多用户多样化场景下的数值结果表明，所提方法在满足通信与感知性能指标的同时，实现了高能效。