基于OFDM的ISAC网络中的联合目标获取与精细位置估计
Joint Target Acquisition and Refined Position Estimation in OFDM-based ISAC Networks
浏览论文内容
中文总结 AI 辅助
本文提出一种两阶段框架，在OFDM的ISAC网络中通过基站协作和最大似然估计实现目标获取与厘米级精细定位。
中文摘要 AI 辅助
本文研究了在基于OFDM的集成感知与通信（ISAC）网络中，通过融合中心进行基站（BS）协作的联合目标获取与位置估计问题。提出了一种两阶段框架：在第一阶段，每个基站计算距离-角度图以检测目标并估计粗略位置，利用空间分集。在第二阶段，通过共享全局参考框架内的预定义感兴趣区域（RoIs）上的协作最大似然（ML）估计器进行精细定位。数值结果表明，所提出的方法不仅通过基站协作提高了检测性能，而且实现了厘米级的定位精度，突显了精细估计技术的有效性。
英文摘要
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.
