Reconfigurable Intelligent Surface Aided Position and Orientation Estimation Based on Joint Beamforming With Limited Feedback

The sparsity of millimeter wave (mmWave) channels in angular and temporal domains has been exploited for channel estimation, while the associated channel parameters can be utilized for localization. However, line-of-sight (LoS) blockage makes localization highly challenging, which may lead to big positioning inaccuracy. One promising solution is to employ reconfigurable intelligent surfaces (RIS) to generate virtual line-of-sight (VLoS) paths. Hence, it is essential to investigate the wireless positioning in RIS-aided mmWave systems. In this paper, an adaptive joint LoS and VLoS localization scheme is proposed, where the VLoS is constructed by a beamforming protocol operated between RIS and mobile station (MS). More specifically, to sense the location and orientation of a MS, a novel interlaced scanning beam sweeping algorithm is proposed to acquire the optimal beams. In this algorithm, LoS and VLoS paths are separately estimated and the optimal beams are selected according to the received signal strength so as to mitigate the LoS blockage problem. Then, based on the selected beams and received signal strength, angle of arrival (AoA), angle of departure (AoD), angle of reflection (AoR) and time of arrival (ToA) are estimated. Finally, with the aid of these estimated parameters, the location and orientation of the MS are estimated. We derive the Cramer-Rao lower bounds (CRLBs) for both location estimation and orientation estimation, and compare them with the corresponding results obtained from simulations. We compare the performance of our proposed scheme with that attained by three legacy schemes, when various aspects are considered. The performance results show the superiority of our proposed beam training algorithm, which is capable of achieving a localization error within 15 cm and an orientation error within 0.003 rads. Furthermore, the training overhead is 100 times less than that of the conventional exhaustive search algorithm, while obtaining a 13 dBm power gain when compared with the hierarchical codebook based search algorithm.