标题： ISAC系统中扩展目标基于参数散射模型的CRB优化 显示英文标题

标题： CRB Optimization using a Parametric Scattering Model for Extended Targets in ISAC Systems

摘要： 本文提出了一种新颖的参数化散射模型（PSM），用于集成感知与通信（ISAC）系统中的扩展目标感知。 该 PSM 通过紧凑的关键参数集（包括中心角和角度扩展）高效捕获目标的角度特性，解决了传统模型的局限性，从而实现高效的优化。 基于 PSM，我们首先推导了参数估计的克拉美罗下界（CRB），然后提出了一种波束成形设计算法，在满足通信信号干扰加噪声比（SINR）和功率约束的同时最小化 CRB。 通过将 PSM 集成到波束成形优化过程中，所提出的框架在平衡感知精度和通信质量之间实现了优越的 CRB 性能。 仿真结果表明，基于 PSM 的方法在资源受限场景中始终优于传统的非结构化和离散散射模型，突显了其实际适用性和可扩展性。 显示英文摘要

摘要： This paper presents a novel parametric scattering model (PSM) for sensing extended targets in integrated sensing and communication (ISAC) systems. The PSM addresses the limitations of traditional models by efficiently capturing the target's angular characteristics through a compact set of key parameters, including the central angle and angular spread, enabling efficient optimization. Based on the PSM, we first derive the Cramer-Rao Bound (CRB) for parameter estimation and then propose a beamforming design algorithm to minimize the CRB while meeting both communication signal-to-interference-plus-noise ratio (SINR) and power constraints. By integrating the PSM into the beamforming optimization process, the proposed framework achieves superior CRB performance while balancing the tradeoff between sensing accuracy and communication quality. Simulation results demonstrate that the PSM-based approach consistently outperforms traditional unstructured and discrete scattering models, particularly in resource-limited scenarios, highlighting its practical applicability and scalability.