The target sensing/localization performance is fundamentally limited by the line-of-sight link and severe signal attenuation over long distances. This paper considers a challenging scenario where the direct link between the base station (BS) and the target is blocked due to the surrounding blockages and leverages the intelligent reflecting surface (IRS) with some active sensors, termed as \textit{semi-passive IRS}, for localization. To be specific, the active sensors receive echo signals reflected by the target and apply signal processing techniques to estimate the target location. We consider the joint time-of-arrival (ToA) and direction-of-arrival (DoA) estimation for localization and derive the corresponding Cram\'{e}r-Rao bound (CRB), and then a simple ToA/DoA estimator without iteration is proposed. In particular, the relationships of the CRB for ToA/DoA with the number of frames for IRS beam adjustments, number of IRS reflecting elements, and number of sensors are theoretically analyzed and demystified. Simulation results show that the proposed semi-passive IRS architecture provides sub-meter level positioning accuracy even over a long localization range from the BS to the target and also demonstrate a significant localization accuracy improvement compared to the fully passive IRS architecture.

翻译：目标感知/定位性能从根本上受到视距链路和长距离信号严重衰减的限制。本文考虑基站与目标之间的直射路径因周围障碍物遮挡而受阻的挑战场景，并利用配备有源传感器的智能反射面（称为半被动智能反射面）进行定位。具体而言，有源传感器接收目标反射的回波信号，并应用信号处理技术估计目标位置。我们联合利用到达时间和到达角度进行定位估计，推导相应的克拉美-罗界，并提出一种无需迭代的简单到达时间/到达角度估计器。此外，从理论上分析并揭示了到达时间/到达角度的克拉美-罗界与智能反射面波束调整帧数、反射单元数量以及传感器数量之间的关系。仿真结果表明，所提出的半被动智能反射面架构即使在与基站相距较远的定位范围内也能提供亚米级定位精度，并且与全被动智能反射面架构相比，显著提升了定位精度。