Motion Feature and Millimeter Wave Multi-path AoA-ToA Based 3D Indoor Positioning

Abstract

As location information becomes vitally important, positioning has been a highly desirable feature of 5G system which enables a huge amount of location-based applications and services. Millimeter wave (mmWave) is the promising technology for both offering better spectrum resource and positioning performance. A virtualized indoor office scenario with only one mmWave base station (BS) is considered in this paper. User equipment (UE) motion feature, mmWave line-of-sight (LoS) and first order reflection paths' AoA-ToA are fused for indoor positioning. Firstly, an improved least mean square (LMS) algorithm that combines motion message is proposed to refine the multi-path AoA estimation. Furthermore, a modified multi-path unscented Kalman filter (UKF) is proposed to track UE's position in the scenario. The information exchanges of the two stages not only consist of estimates(position, AoA) but also variance of position. Based on the simulation results, the proposed methods provide 2 times LoS-AoA estimation gains and centimeter 3D positioning accuracy respectively. Besides, this strategy is capable of positioning task with insufficient anchor nodes (AN).