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Reconfigurable Intelligent Surfaces-assisted Positioning in Integrated Sensing and Communication Systems

Huyen-Trang Ta, Ngoc-Son Duong, Trung-Hieu Nguyen, Van-Linh Nguyen, Thai-Mai Dinh

TL;DR

The paper tackles high-precision target localization in RIS-assisted ISAC by leveraging dual echoes from a direct path and a RIS-reflected path. It introduces a two-stage strategy: a coarse estimation using direct and RIS path dictionaries to initialize angular and distance parameters, followed by a fast iterative refinement that decouples linear gains from nonlinear geometry via a separable least-squares approach and a damped Levenberg update. The core contributions are the joint parameter-position estimation framework, a closed-form gain update for fixed position, and a linearized, low-complexity refinement that maintains CRLB-like accuracy at moderate-to-high SNRs. Simulation results demonstrate comparable accuracy to traditional non-linear estimators with substantial complexity reductions, highlighting practical viability for RIS-enabled ISAC systems in 6G scenarios.

Abstract

This paper investigates the problem of high-precision target localization in integrated sensing and communication (ISAC) systems, where the target is sensed via both a direct path and a reconfigurable intelligent surface (RIS)-assisted reflection path. We first develop a sequential matched-filter estimator to acquire coarse angular parameters, followed by a range recovery process based on subcarrier phase differences. Subsequently, we formulate the target localization problem as a non-linear least squares optimization, using the coarse estimates to initialize the target's position coordinates. To solve this efficiently, we introduce a fast iterative refinement algorithm tailored for RIS-aided ISAC environments. Recognizing that the signal model involves both linear path gains and non-linear geometric dependencies, we exploit the separable least-squares structure to decouple these parameters. Furthermore, we propose a modified Levenberg algorithm with an approximation strategy, which enables low-cost parameter updates without necessitating repeated evaluations of the full non-linear model. Simulation results show that the proposed refinement method achieves accuracy comparable to conventional approaches, while significantly reducing algorithmic complexity.

Reconfigurable Intelligent Surfaces-assisted Positioning in Integrated Sensing and Communication Systems

TL;DR

The paper tackles high-precision target localization in RIS-assisted ISAC by leveraging dual echoes from a direct path and a RIS-reflected path. It introduces a two-stage strategy: a coarse estimation using direct and RIS path dictionaries to initialize angular and distance parameters, followed by a fast iterative refinement that decouples linear gains from nonlinear geometry via a separable least-squares approach and a damped Levenberg update. The core contributions are the joint parameter-position estimation framework, a closed-form gain update for fixed position, and a linearized, low-complexity refinement that maintains CRLB-like accuracy at moderate-to-high SNRs. Simulation results demonstrate comparable accuracy to traditional non-linear estimators with substantial complexity reductions, highlighting practical viability for RIS-enabled ISAC systems in 6G scenarios.

Abstract

This paper investigates the problem of high-precision target localization in integrated sensing and communication (ISAC) systems, where the target is sensed via both a direct path and a reconfigurable intelligent surface (RIS)-assisted reflection path. We first develop a sequential matched-filter estimator to acquire coarse angular parameters, followed by a range recovery process based on subcarrier phase differences. Subsequently, we formulate the target localization problem as a non-linear least squares optimization, using the coarse estimates to initialize the target's position coordinates. To solve this efficiently, we introduce a fast iterative refinement algorithm tailored for RIS-aided ISAC environments. Recognizing that the signal model involves both linear path gains and non-linear geometric dependencies, we exploit the separable least-squares structure to decouple these parameters. Furthermore, we propose a modified Levenberg algorithm with an approximation strategy, which enables low-cost parameter updates without necessitating repeated evaluations of the full non-linear model. Simulation results show that the proposed refinement method achieves accuracy comparable to conventional approaches, while significantly reducing algorithmic complexity.
Paper Structure (18 sections, 1 theorem, 40 equations, 3 figures, 1 algorithm)

This paper contains 18 sections, 1 theorem, 40 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and problem formulation
  3. Proposed Coarse Channel Estimation Method
  4. Direct Path Dictionary
  5. RIS-assisted path dictionary
  6. Channel Estimation
  7. Path gain estimation
  8. Distance estimation
  9. Proposed method for joint parameter and target position estimation
  10. Finding $g_{\mathrm{d}}$, $g_{\mathrm{r}}$, given $\mathbf{p}$
  11. Finding $\mathbf{p}$, given $g_{\mathrm{d}}, g_{\mathrm{r}}$
  12. Simulation Results and Discussion
  13. Simulation Setup
  14. Simulation results and discussion
  15. On the coarse estimation
  16. ...and 3 more sections

Key Result

Theorem 2.1

If the paths are completely independent of each other, we have where $R$ and $\mathrm{PEB}$ are number of path and position error bound (PEB), respectively.

Figures (3)

  • Figure 1: Geometric scenario of the RIS-aided ISAC sensing with a direct path and an RIS-assisted path.
  • Figure 2: Coarse estimate evaluation
  • Figure 3: Refinement evaluation

Theorems & Definitions (1)

  • Theorem 2.1