This study explores the use of non-line-of-sight (NLOS) components in millimeter-wave (mmWave) communication systems for joint localization and environment sensing. The radar cross section (RCS) of a reconfigurable intelligent surface (RIS) is calculated to develop a general path gain model for RISs and traditional scatterers. The results show that RISs have a greater potential to assist in localization due to their ability to maintain high RCSs and create strong NLOS links. A one-stage linear weighted least squares estimator is proposed to simultaneously determine user equipment (UE) locations, velocities, and scatterer (or RIS) locations using line-of-sight (LOS) and NLOS paths. The estimator supports environment sensing and UE localization even using only NLOS paths. A second-stage estimator is also introduced to improve environment sensing accuracy by considering the nonlinear relationship between UE and scatterer locations. Simulation results demonstrate the effectiveness of the proposed estimators in rich scattering environments and the benefits of using NLOS paths for improving UE location accuracy and assisting in environment sensing. The effects of RIS number, size, and deployment on localization performance are also analyzed.

翻译：本研究探索了在毫米波通信系统中利用非视距（NLOS）分量实现联合定位与环境感知。通过计算可重构智能表面（RIS）的雷达散射截面（RCS），建立了适用于RIS与传统散射体的通用路径增益模型。结果表明，由于RIS能够维持高RCS并创建强NLOS链路，其在辅助定位方面具有更大潜力。提出了一种单阶段线性加权最小二乘估计器，利用视距（LOS）与NLOS路径同时确定用户设备（UE）位置、速度以及散射体（或RIS）位置。该估计器即使仅依靠NLOS路径也能支持环境感知与UE定位。为提升环境感知精度，进一步引入考虑UE与散射体位置非线性关系的第二阶段估计器。仿真结果验证了所提估计器在丰富散射环境中的有效性，以及利用NLOS路径改善UE定位精度并辅助环境感知的优势。此外，还分析了RIS数量、尺寸及部署方式对定位性能的影响。