RIS-Position and Orientation Estimation in Dispersive mmWave MIMO Scenarios

In this paper, we investigate the problem of estimating the position and the angle of rotation of a mobile station (MS) in a millimeter wave (mmWave) multiple-input-multiple-output (MIMO) system aided by a reconfigurable intelligent surface (RIS). The virtual line-of-sight (VLoS) link created by the RIS and the non-line-of-sight (NLoS) links that originate from scatterers in the considered environment are utilized to facilitate the estimation. A two-step positioning scheme is exploited, where the channel parameters are first acquired, and the position-related parameters are then estimated. The channel parameters are obtained through a coarser and a subsequent finer estimation processes. As for the coarse estimation, the distributed compressed sensing orthogonal simultaneous matching pursuit (DCS-SOMP) algorithm, the maximum likelihood (ML) algorithm, and the discrete Fourier transform (DFT) are utilized to separately estimate the channel parameters. The obtained channel parameters are then jointly refined by using the space-alternating generalized expectation maximization (SAGE) algorithm, which circumvents the high-dimensional optimization issue of ML estimation. Departing from the estimated channel parameters, the positioning-related parameters are estimated. The performance of estimating the channel-related and position-related parameters is theoretically quantified by using the Cramer-Rao lower bound (CRLB). Simulation results demonstrate the superior performance of the proposed positioning algorithms.