RIS Nearfield Position and Velocity Estimation Using a Validated Propagation Model

TL;DR

This paper presents a robust multi-step algorithm for accurate indoor position and velocity estimation using reconfigurable intelligent surfaces, accounting for antenna patterns and near-field effects in NLOS scenarios.

Contribution

It introduces a validated propagation model and a modified estimation algorithm that improves accuracy in RIS-based indoor localization.

Findings

Achieves 7 mm position error at 2 m distance

Attains 0.12 m/s velocity error under realistic conditions

Demonstrates robustness of the proposed algorithm in NLOS scenarios

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.