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限于视距链路以及长距离下的严重信号衰减。本文考虑基站与目标之间的直视链路因周围遮挡物而被阻塞的挑战性场景，利用具有部分有源传感器的智能反射面（称为半无源IRS）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。我们考虑联合到达时间（ToA）与到达方向（DoA）估计进行定位，推导了相应的克拉美-罗界（CRB），并提出了一种无需迭代的简单ToA/DoA估计器。特别地，从理论上分析和揭示了ToA/DoA的CRB与IRS波束调整帧数、IRS反射单元数量及传感器数量之间的关系。仿真结果表明，所提出的半无源IRS架构即使在基站到目标的长距离定位场景下也能提供亚米级定位精度，并且与全无源IRS架构相比，定位精度显著提升。