Integrated sensing and communication (ISAC) relies on monostatic sensing (MS) and bistatic positioning (BP) to enable comprehensive environmental awareness and user localization. However, existing frameworks predominantly assume static geometries and optimize these modalities independently, neglecting user mobility and sequential information sharing. In this paper, we propose a velocity-aware sequential beamforming framework that dynamically couples MS and BP in time. We derive the Cramer-Rao bounds (CRBs) in the position domain to formulate a non-convex resource allocation problem. Instead of relying on static weighted-sum tradeoffs, we introduce a sequential Bayesian optimization strategy where MS is executed first to construct a reliable structural prior on the UE and passive targets (PTs). This covariance prior is subsequently passed to the UE to regularize the BP estimation stage. We demonstrate that optimizing a single shared beamformer globally across both phases yields superior synergistic gains compared to a two-stage greedy approach. Simulation results validate that the shared sequential design efficiently balances limited symbol resources, achieving centimeter-level positioning accuracy for both the UE and PTs, robust velocity estimation, and a significantly reduced computational runtime.

翻译：集成感知与通信（ISAC）依赖于单站感知（MS）和双站定位（BP）实现全面的环境感知和用户定位。然而，现有框架主要假设静态几何结构并独立优化这些模态，忽视了用户移动性和时序信息共享。本文提出一种速度感知的时序波束成形框架，在时间维度上动态耦合MS和BP。我们推导了位置域中的克拉美-罗界（CRB），以构建一个非凸资源分配问题。不同于依赖静态加权折衷策略，我们引入一种序贯贝叶斯优化方法：首先执行MS构建关于用户设备和无源目标（PTs）的可靠结构先验，随后将该协方差先验传递给用户设备以正则化BP估计阶段。研究表明，相较于两阶段贪婪方法，全局联合优化两个阶段的共享波束成形器可产生更优的协同增益。仿真结果验证了该共享时序设计方案能高效平衡有限的符号资源，实现用户设备与PTs的厘米级定位精度、稳健的速度估计，并显著降低计算耗时。