Joint Target Acquisition and Refined Position Estimation in OFDM-based ISAC Networks

TL;DR

This paper presents a two-stage cooperative framework for joint target detection and high-precision localization in OFDM-based ISAC networks, utilizing BS cooperation and ML estimation to achieve centimeter-level accuracy.

Contribution

It introduces a novel two-stage cooperative localization method combining initial detection with refined ML-based position estimation in ISAC networks.

Findings

Enhanced detection performance through BS cooperation

Achieved centimeter-level localization accuracy

Effective refined estimation within predefined RoIs

Abstract

This paper addresses joint target acquisition and position estimation in an OFDM-based integrated sensing and communication (ISAC) network with base station (BS) cooperation via a fusion center. A two-stage framework is proposed: in the first stage, each BS computes range-angle maps to detect targets and estimate coarse positions, exploiting spatial diversity. In the second stage, refined localization is performed using a cooperative maximum likelihood (ML) estimator over predefined regions of interest (RoIs) within a shared global reference frame. Numerical results demonstrate that the proposed approach not only improves detection performance through BS cooperation but also achieves centimeter-level localization accuracy, highlighting the effectiveness of the refined estimation technique.