Accurate localization of mobile terminals is crucial for integrated sensing and communication systems. Existing fingerprint localization methods, which deduce coordinates from channel information in pre-defined rectangular areas, struggle with the heterogeneous fingerprint distribution inherent in non-line-of-sight (NLOS) scenarios. To address the problem, we introduce a novel multi-source information fusion learning framework referred to as the Autosync Multi-Domain 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 fingerprint 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 demonstrate that AMDNLoc significantly enhances localization accuracy by over 40\% compared with traditional convolutional neural networks on the wireless artificial intelligence research dataset.

翻译：移动终端的精确定位对于集成感知与通信系统至关重要。现有的指纹定位方法通过预定义矩形区域内的信道信息推导坐标，难以应对非视距场景中固有的异构指纹分布问题。为解决该问题，本文提出了一种新颖的多源信息融合学习框架，称为自动同步多域非视距定位（AMDNLoc）。具体而言，AMDNLoc采用融合目标跟踪算法的两级匹配滤波器与基于迭代质心的聚类方法，自动且非规则地划分非视距区域，确保信道状态信息在频域、功率域和时延域中具有均匀的指纹分布。此外，该框架利用区域特定的线性分类器阵列，结合基于深度残差网络的特征提取与融合技术，建立这些区域内指纹特征与坐标的关联函数。仿真结果表明，在无线人工智能研究数据集上，AMDNLoc相较于传统卷积神经网络将定位精度显著提升了40%以上。