RIS Nearfield Position and Velocity Estimation Using a Validated Propagation Model

TL;DR

This work addresses accurate near-field localization using RIS in indoor NLOS scenarios by evaluating a composite RIS with four tiles and 1-bit phase control under a validated propagation model. The authors modify a snapshot-based three-step localization approach: replacing a far-field grid search with a robust near-field 3D grid search, incorporating a closed-form refinement, and applying a 6D gradient-descent search, all while accounting for antenna patterns in the propagation model. The proposed pipeline achieves high precision, demonstrating a position error of $7\,\mathrm{mm}$ and a velocity error of $0.12\,\mathrm{m/s}$ at a distance of $2\,\mathrm{m}$ from the RIS center, using a $3\times3$ m area of interest and realistic mmWave RIS settings. The results also quantify how antenna-pattern effects and near-field wavefront curvature influence localization accuracy, underscoring the practical potential of RIS-enabled NF localization for industrial automation and control.

Abstract

We investigate reconfigurable intelligent surfaces (RISs) for the task of position and velocity estimation in non-LOS (NLOS) indoor scenarios, using a snapshot based multi-step estimation algorithm. We evaluate a compound RIS structure prototype composed of four RIS tiles with 1-bit phase control per RIS unit cell. Numerical simulation results taking the antenna patterns into account are presented for an 3 m x 3 m area of interest. We demonstrate that the initial grid search step using the far field assumption is not robust enough for small distances to the RIS center and propose a more robust algorithm. Furthermore, we show that the effect of the antenna pattern causes an increased position and velocity error. Our modified three-step algorithm achieves a position error of 7 mm and a velocity error of 0.12 m/s at a distance of 2 m to the RIS center under a realistic numerical propagation model.

Figures (5)

• Figure 1: RIS coordinate system for a hexagonal RIS placement in the yz-plane. The BS horn antenna radiates from position ${\hbox{\boldmath$p$}}_\text{BS}$ towards the center of the RIS at ${\hbox{\boldmath$q$}}_\text{r}={\hbox{\boldmath$0$}}=[0,0,0]^\text{T}$ over a distance of $|{\hbox{\boldmath$p$}}_\text{BS}|$, similarly the omni-directional UE antenna at position ${\hbox{\boldmath$p$}}$ is within a distance of $|{\hbox{\boldmath$p$}}|$. The LOS link between BS and UE is blocked by an object. The center of the square RIS unit cells are at ${\hbox{\boldmath$q$}}_m$. The RIS is shown enlarged to improve clarity.
• Figure 2: RIS composed of four RIS tiles with each 127 elements, resulting in a total number of $M=508$ elements.
• Figure 3: Geometrical configuration of RIS, BS and AOI for the UE. The translucent blue cube blocks the LOS between BS and UE.
• Figure 4: RMSVE, RMSPE, and average SNR in the 2D AOI. The RMS errors are obtained after the 6D gradient descent search averaged over $F=250$ runs.
• Figure 5: RMSVe and RMSPE versus the distance to the center of the RIS. We evaluate the three algorithm steps (i) grid search (GS), (ii) closed-form refinement (CF), and (iii) 6D gradient decent search (6D). We compare the GS using the FF assumption (GS FF) with the more robust GS using the NF assumption (GS NF). Furthermore we show the GS in the NF followed by a finer GS step (GS NF fine). Finally, we also depict the result without antenna pattern (6D (GS NF fine), noAP).