OFDM系统中面向多点非视距定位的多源融合学习方法
Multi-Sources Fusion Learning for Multi-Points NLOS Localization in OFDM System
AI总结：
本文提出AMDNLoc多源融合学习框架，通过匹配滤波、目标跟踪和迭代聚类分割非视距区域，并结合深度残差网络特征提取与分段线性分类器，在OFDM系统中实现1.46米的高精度多点非视距定位。
摘要：
移动终端的精确定位是集成感知与通信系统的关键环节。传统的基于指纹的定位方法在预设矩形区域内从信道信息推断坐标，但在非视距（NLOS）场景中，尤其是在正交频分复用系统中，由于指纹分布不均匀，常常面临挑战。为克服这一局限，我们提出了一种新颖的多源信息融合学习框架，称为Autosync多域非视距定位（AMDNLoc）。具体而言，AMDNLoc采用两级匹配滤波器，结合目标跟踪算法和基于迭代质心的聚类，自动且不规则地分割非视距区域，确保信道状态信息在频率、功率和时延域内均匀分布。此外，该框架利用分段特定线性分类器阵列，结合基于深度残差网络的特征提取与融合，建立区域内指纹特征与坐标之间的相关函数。仿真结果表明，AMDNLoc在典型无线人工智能研究数据集上实现了1.46米的非视距定位精度，并在可解释性、适应性和可扩展性方面表现出显著提升。
英文摘要：
Accurate localization of mobile terminals is a pivotal aspect of integrated sensing and communication systems. Traditional fingerprint-based localization methods, which infer coordinates from channel information within pre-set rectangular areas, often face challenges due to the heterogeneous distribution of fingerprints inherent in non-line-of-sight (NLOS) scenarios, particularly within orthogonal frequency division multiplexing systems. To overcome this limitation, we develop a novel multi-sources information fusion learning framework referred to as the Autosync Multi-Domains NLOS Localization (AMDNLoc). Specifically, AMDNLoc employs a two-stage matched filter fused with a target tracking algorithm and iterative centroid-based clustering to automatically and irregularly segment NLOS regions, ensuring uniform distribution within channel state information across frequency, power, and time-delay domains. Additionally, the framework utilizes a segment-specific linear classifier array, coupled with deep residual network-based feature extraction and fusion, to establish the correlation function between fingerprint features and coordinates within these regions. Simulation results reveal that AMDNLoc achieves an impressive NLOS localization accuracy of 1.46 meters on typical wireless artificial intelligence research datasets and demonstrates significant improvements in interpretability, adaptability, and scalability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
