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

2026-02-16

AI Summary

This work addresses the challenge of high-precision localization in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication systems, where joint exploitation of direct and reflected paths introduces significant computational complexity. To tackle this, the authors propose a low-complexity fast iterative refinement algorithm. The method first obtains coarse angle estimates via sequential matched filtering, then recovers range information by leveraging phase differences across subcarriers. The localization problem is formulated as a nonlinear least squares optimization. Crucially, the approach exploits a separable least squares structure to decouple linear and nonlinear parameters and incorporates an improved Levenberg–Marquardt algorithm that avoids repeated full-model evaluations. Experimental results demonstrate that the proposed method achieves localization accuracy comparable to conventional approaches while substantially reducing computational complexity.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Reconfigurable Intelligent Surfaces
• Integrated Sensing and Communication
• Target Localization
• High-precision Positioning
• RIS-assisted Reflection

Innovation

• Reconfigurable Intelligent Surface (RIS)
• Integrated Sensing and Communication (ISAC)
• Separable Least Squares
• Iterative Refinement
• Levenberg Algorithm