Rigid Body Localization and Environment Sensing with 5G Millimeter Wave MIMO

Abstract

Accurately localizing a moving target (MT) assisted with 5G in indoor environment could enable a wide variety of new applications. However, an MT in 3-dimensional space is usually considered as a rigid body with six degrees of freedom for industrial applications. Furthermore, the radio-based localization suffers from the non-line-of-sight (NLOS) condition in indoor scenes due to the uncertain environments, which proves to be a main source of location error. To improve the rigid body localization accuracy as well as unravel useful environmental information from the received signals, a novel rigid body localization and environment sensing scheme is proposed in this paper. The angle of arrivals (AOAs) derived from 5G channel estimation combined with singular value decomposition (SVD) method is adopted to achieve rigid body position and orientation estimation. Also, we propose a reflection point estimation method by leveraging a hierarchical iterative maximum likelihood-DCS-SOMP (HIML-DCS-SOMP) algorithm to extract the angular information of the single-bounce specular reflections. Simulation results demonstrate that the proposed scheme can achieve high accuracy rigid body localization and sketch the environment information in indoor scene.