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

TL;DR

This paper presents a novel high-precision target localization method in integrated sensing and communication systems using reconfigurable intelligent surfaces, combining coarse estimation and efficient iterative refinement to improve accuracy and reduce complexity.

Contribution

It introduces a fast iterative refinement algorithm leveraging RIS-assisted signals, with a modified Levenberg method for low-cost parameter updates in ISAC environments.

Findings

Achieves high localization accuracy comparable to traditional methods.

Significantly reduces computational complexity of the refinement process.

Demonstrates effectiveness through simulation results.

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.