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对高精度、低延迟的定位服务需求急剧增长。尽管蜂窝网络具备目标探测与定位的潜力，但实现精准可靠的定位仍面临关键挑战。特别地，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值存在差异性。为解决此问题，我们提出一种利用智能反射表面（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策略性部署于目标（如车辆和机器人）上，可实现多测量值的有效关联并促进定位过程。我们旨在通过联合优化目标关联、IRS相移和驻留时间，最小化目标的最大克拉美-罗下界（CRLB）。然而，由于目标函数的非凸性及变量间的强耦合性，求解该CRLB优化问题并非易事。针对单目标定位场景，当基站（BS）可灵活部署时，给出了简化的闭式表达式，并推导出最优基站位置以提供原问题的性能下界……