Near-Field Tracking for Reconfigurable Intelligent Surface Aided Systems Harnessing the NLoS Paths

In this paper, the near-field tracking problem harnessing the non-line-of-sight (NLoS) paths is investigated for reconfigurable intelligent surface (RIS)-aided systems. In order to reduce the complexity of the position estimation, the near-field signal model, where the virtual line-of-sight (VLoS) paths and NLoS paths coexist, is constructed by employing the subarray far-field model. A probability transition model for the tracking problem is established, as well as the corresponding factor graph. Then, a low complexity near-field tracking algorithm based on message passing is developed to jointly estimate the UE's and scatterers' positions in each time slot. Numerical results show that the mean square error (MSE) of the proposed algorithm performs close to the misspecified Cramér-Rao Lower Bound (MCRLB), and validate the performance gain of harnessing the NLoS paths.