Autonomous driving and intelligent transportation applications have dramatically increased the demand for high-accuracy and low-latency localization services. While cellular networks are potentially capable of target detection and localization, achieving accurate and reliable positioning faces critical challenges. Particularly, the relatively small radar cross sections (RCS) of moving targets and the high complexity for measurement association give rise to weak echo signals and discrepancies in the measurements. To tackle this issue, we propose a novel approach for multi-target localization by leveraging the controllable signal reflection capabilities of intelligent reflecting surfaces (IRSs). Specifically, IRSs are strategically mounted on the targets (e.g., vehicles and robots), enabling effective association of multiple measurements and facilitating the localization process. We aim to minimize the maximum Cram\'er-Rao lower bound (CRLB) of targets by jointly optimizing the target association, the IRS phase shifts, and the dwell time. However, solving this CRLB optimization problem is non-trivial due to the non-convex objective function and closely coupled variables. For single-target localization, a simplified closed-form expression is presented for the case where base stations (BSs) can be deployed flexibly, and the optimal BS location is derived to provide a lower performance bound of the original problem ...

翻译：自动驾驶与智能交通应用极大提升了对高精度、低延迟定位服务的需求。尽管蜂窝网络具备目标检测与定位的潜在能力，但实现精确可靠的定位仍面临严峻挑战。特别是运动目标相对较小的雷达散射截面（RCS）以及测量关联的高复杂度，导致回波信号微弱且测量结果存在差异。为解决这一问题，我们提出了一种利用智能反射面（IRS）可控信号反射能力的新型多目标定位方法。具体而言，将IRS策略性地安装在目标（如车辆和机器人）上，能够有效关联多个测量结果，并促进定位过程。我们旨在通过联合优化目标关联、IRS相移和驻留时间，最小化目标的克拉美-罗下界（CRLB）最大值。然而，由于目标函数非凸且变量紧密耦合，求解此CRLB优化问题并非易事。针对单目标定位，我们给出了基站（BS）可灵活部署情况下的简化闭式表达式，并推导了最优BS位置，为原问题提供了性能下界...