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 55% compared with traditional convolutional neural network on the wireless artificial intelligence research dataset.

翻译：移动终端的精确定位对于通感一体化系统至关重要。现有指纹定位方法通过预定义矩形区域内的信道信息推断坐标，难以应对非视距（NLOS）场景中固有的指纹分布异质性。针对此问题，本文提出一种新颖的多源信息融合学习框架——自动同步多域非视距定位（AMDNLoc）。具体而言，AMDNLoc采用两级匹配滤波器融合目标跟踪算法与迭代质心聚类，自动不规则分割NLOS区域，确保信道状态信息在频域、功率域和时延域内的指纹分布均匀性。同时，该框架利用区域特定的线性分类器阵列，结合深度残差网络的特征提取与融合，建立这些区域中指纹特征与坐标之间的关联函数。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可将定位精度提升超过55%。