Millimeter Wave LOS/NLOS Identification and Localization via Mean-Shift Clustering

Abstract

In complex scenarios where line-of-sight (LOS) and non-line-of-sight (NLOS) paths both exist, a LOS/NLOS identifi-cation method is necessary. In this paper, we propose a millimeter wave (mmWave) LOS/NLOS identification scheme utilizing mean-shift (MS) clustering algorithm and a 3D angle-of-arrival (AOA) localization algorithm using both LOS and one-bound reflection NLOS paths. In order to separate LOS and one-bound NLOS paths from multiple-bound NLOS paths, we first make all possible reflection condition assumptions for all paths to give all possible user equipment (UE) locations. Each path’s assumption corresponds to one possible UE location. Then by applying mean-shift clustering to the calculated locations, we find the cluster with the most points as the set of correct hypothetical points. For the points in this cluster, the corresponding LOS/NLOS assumptions are considered to be correct, which means the LOS/NLOS conditions are successfully identified. Given known reflection conditions and original AOA measurements, the position estimate is then solved by the proposed AOA localization algorithm. Simulation results demonstrate that our scheme is capable of achieving high identification accuracy and localization precision.