RIS- and Multi-Snapshot-Enabled SISO 3D Position and Velocity Estimation With Single Base Station

2025-09-27

AI Summary

Single-base-station SISO systems cannot achieve three-dimensional joint position and velocity estimation (3D-JPVE) due to insufficient degrees of freedom. Method: This paper proposes a novel cooperative mechanism integrating reconfigurable intelligent surfaces (RIS) with multi-snapshot processing. We theoretically prove that neither a single RIS nor multi-snapshots alone suffices for 3D-JPVE—both must be jointly exploited. A two-stage low-complexity algorithm is designed: (i) constructing a third-order received signal tensor and estimating angles and Doppler shifts via tensor decomposition; (ii) iteratively refining channel parameters and kinematic states through differential linear estimation followed by maximum-likelihood optimization. Contribution/Results: Under active RIS deployment, the proposed method asymptotically achieves the Cramér–Rao lower bound (CRLB). Simulations demonstrate significant improvements in both localization and velocity estimation accuracy over state-of-the-art approaches, while maintaining statistical efficiency and engineering feasibility.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Motion & Tracking

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Reconfigurable intelligent surface (RIS) panels can act as cost-effective anchors for radio localization, complementing conventional base station (BS) anchors. This paper investigates joint three-dimensional position and velocity estimation (3D-JPVE) in single-input single-output (SISO) systems with only one BS available. We first theoretically show that 3D-JPVE is infeasible when relying solely on a single RIS or on multiple snapshots alone. To address this, we propose combining RIS deployment with multi-snapshot utilization to enable realizable 3D-JPVE. A two-stage method is developed for multi-snapshot channel parameter estimation, comprising a tensor-based coarse estimation step followed by a maximum likelihood refinement step. In particular, we introduce a third-order tensor formulation to decompose the challenging 3D joint angle-of-departure and Doppler shift estimation (3D-JADE) into two tractable subproblems, which are jointly solved via a low-complexity alternating optimization approach. Building on the channel parameter estimates, we further design a two-stage low-complexity method for optimal 3D-JPVE: coarse estimation is obtained from differential measurements through linear equations, and the preliminary results are refined iteratively using the original measurements. Moreover, we derive the closed-form Cramer-Rao lower bound (CRLB) and show that the proposed 3D-JPVE method approaches CRLB-level accuracy. Simulation results confirm the statistical efficiency of the proposed estimators and demonstrate substantial 3D-JPVE performance gains when deploying active RIS compared to passive RIS.

Problem

• Enables 3D position and velocity estimation with single base station
• Solves infeasibility by combining RIS deployment with multi-snapshot data
• Develops low-complexity tensor methods for joint angle and Doppler estimation

Innovation

• Combining RIS deployment with multi-snapshot utilization
• Using tensor-based coarse estimation with maximum likelihood refinement
• Employing two-stage low-complexity method with differential measurements