AI 中文总结
本文研究LEO卫星辅助地面多基地ISAC网络的三维目标定位，通过PPP和Walker模型推导混合CRLB缩放规律，并提出低复杂度的贪心卫星选择策略，性能接近穷举搜索。
AI 中文摘要
低地球轨道（LEO）卫星为增强天地一体化网络（ISTNs）中的三维（3-D）感知提供了高仰角且空间多样化的视角。本文研究了一个LEO辅助的地面多基地集成感知与通信（ISAC）网络，用于三维目标定位，其中多颗LEO卫星作为协作感知照射源，为分布式地面雷达接收机提供额外的双基地观测。我们首先将协作卫星建模为齐次泊松点过程（PPP），并推导了平均混合克拉美-罗下界（CRLB）的易处理近似。所得的缩放规律表明，对于固定的协作区域，根CRLB随协作卫星平均数量的平方根倒数而减小，而在固定卫星密度下增大协作半径则产生对数递减收益。随后，我们开发了一个考虑地球曲率的Walker模型，该模型结合了轨道结构、卫星运动、可见性和时变感知几何，并推导了相应混合CRLB的易处理近似。获得了边际增益的解析界限以及候选卫星排序的充分条件。基于这些结果，提出了一种面向CRLB的贪心卫星选择策略，以考虑依赖于信干噪比（SCNR）的可靠性和与地面感知配置的几何互补性。所提出的策略始终优于基准方法，并以显著较低的复杂度接近穷举搜索性能。蒙特卡洛仿真验证了两种模型的解析近似。
英文摘要
Low Earth orbit (LEO) satellites provide elevated and spatially diverse viewpoints for enhancing three-dimensional (3-D) sensing in integrated satellite-terrestrial networks (ISTNs). This paper investigates a LEO-assisted terrestrial multistatic integrated sensing and communication (ISAC) network for 3-D target localisation, where multiple LEO satellites act as cooperative sensing illuminators and provide additional bistatic observations to distributed terrestrial radar receivers. We first model cooperative satellites as a homogeneous Poisson point process (PPP) and derive a tractable approximation of the average hybrid Cramér-Rao lower bound (CRLB). The resulting scaling laws show that the root-CRLB decreases with the inverse square root of the mean number of cooperative satellites for a fixed cooperation region, while increasing the cooperation radius at fixed satellite density yields logarithmic diminishing returns. We then develop an Earth-curvature-aware Walker model incorporating orbital structure, satellite motion, visibility, and time-varying sensing geometry, and derive a tractable approximation of the corresponding hybrid CRLB. Analytical bounds on the marginal gain and a sufficient condition for ordering candidate satellites are obtained. Based on these results, a CRLB-oriented greedy satellite-selection strategy is proposed to account for SCNR-dependent reliability and geometric complementarity with the terrestrial sensing configuration. The proposed strategy consistently outperforms benchmarks and approaches exhaustive-search performance with substantially lower complexity. Monte Carlo simulations validate the analytical approximations for both models.
