Digital twin (DT) networks require tight integration with wireless sensing, yet the fundamental limits of such coupling in cell-free integrated sensing and communication (ISAC) systems remain largely unexplored, particularly in the presence of fluid intelligent metasurfaces (FIM). This paper establishes a joint position-velocity Cramer-Rao bound (CRB) framework, operationalized through a twin-in-the-loop architecture. By leveraging a scatter-matrix decomposition of the velocity Fisher information, we show that single-base-station systems are inherently rank-deficient for two-dimensional velocity estimation, whereas cell-free deployments with multiple access-point pairs achieve full observability. The resulting CRB reveals a spatio-temporal decoupling: FIM shape optimization significantly improves position accuracy but does not affect the velocity CRB under isotropic waveforms while Doppler coupling asymmetrically enhances position estimation accuracy. Building on this analysis, we develop a closed-loop DT framework, deriving the critical mismatch angle in closed form and showing that angular diversity in cell-free systems mitigates DT prediction errors. We further characterize the optimal synchronization period and propose a confidence-aware scheduling strategy that reduces the DT update rate. Numerical results demonstrate substantial performance gains over single-base-station systems, with improvements attributed to angular diversity, Doppler-position coupling, and FIM adaptation.

翻译：数字孪生网络需与无线感知深度融合，然而在流体智能超表面存在的无蜂窝通感一体化系统中，这种耦合的极限性能尚未得到充分探索。本文建立了联合位置-速度的克拉美-罗界分析框架，并通过孪生闭环架构实现其工程化应用。通过引入速度费舍尔信息的散射矩阵分解，我们证明单基站系统对二维速度估计存在固有秩亏，而具有多接入点对的无蜂窝部署可实现完全可观性。由此导出的克拉美-罗界揭示了时空解耦特性：流体智能超表面形状优化虽能显著提升位置估计精度，但在各向同性波形下不影响速度克拉美-罗界，而多普勒耦合则非对称地增强位置估计精度。基于该分析，我们构建了闭环数字孪生框架，推导出关键失配角度的闭式表达式，并证明无蜂窝系统中的角度分集可抑制数字孪生预测误差。进一步地，我们刻画了最优同步周期，并提出一种置信度感知调度策略以降低数字孪生更新频率。数值结果表明，相较于单基站系统，所提方案可获得显著性能增益，其提升归因于角度分集、多普勒-位置耦合及流体智能超表面自适应优化。