This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help the base station (BS) provide ISAC services at separate line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive uniform linear array (ULA) IRSsfor sensing, in which each IRS is integrated with dedicated sensors for processing echo signals, and each IRS simultaneously serves one sensing target and one communication user (CU) in its coverage area. In particular, we suppose that the BS sends combined information and dedicated sensing signals for ISAC, and we consider two cases with point and extended targets, in which each IRS aims to estimate the direction-of-arrival (DoA) of the corresponding target and the complete target response matrix, respectively. Under this setup, we first derive the closed-form Cram{\'e}r-Rao bounds (CRBs) for parameters estimation under the two target models. For the point target case, the CRB for AoA estimation is shown to be inversely proportional to the cubic of the number of sensors at each IRS, while for the extended target case, the CRB for target response matrix estimation is proportional to the number of IRS sensors. Next, we consider two different types of CU receivers that can and cannot cancel the interference from dedicated sensing signals prior to information decoding. To achieve fair and optimized sensing performance, we minimize the maximum CRB at all IRSs for the two target cases, via jointly optimizing the transmit beamformers at the BS and the reflective beamformers at the multiple IRSs, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs, the maximum transmit power constraint at the BS, and the unit-modulus constraints at the multiple IRSs.

翻译：本文研究了一种多智能反射面（IRS）辅助的集成感知与通信（ISAC）系统，其中部署多个IRS以帮助基站（BS）在多个视距（LoS）受阻区域提供ISAC服务。我们聚焦于采用半无源均匀线性阵列（ULA）IRS进行感知的场景：每个IRS集成专用传感器处理回波信号，并同时服务于其覆盖区域内的一个感知目标和一个通信用户（CU）。特别地，假设BS发送组合信息和专用感知信号用于ISAC，并考虑点目标与扩展目标两种情形：在点目标情形下，各IRS需估计对应目标的到达角（DoA）；在扩展目标情形下，则需估计完整的目标响应矩阵。基于此设置，我们首先推导了两种目标模型下参数估计的闭式克拉美-罗界（CRB）：对于点目标，到达角估计的CRB与每个IRS的传感器数量的三次方成反比；对于扩展目标，目标响应矩阵估计的CRB与IRS传感器数量成正比。随后，我们考虑两种不同类别的CU接收机：一种能在信息解码前消除专用感知信号的干扰，另一种则不能。为实现公平且优化的感知性能，我们在满足各CU的最小信干噪比（SINR）约束、基站最大发射功率约束以及各IRS的单位模约束条件下，通过联合优化基站的发射波束赋形和多IRS的反射波束赋形，最小化两种目标情形下所有IRS的最大CRB。