文献类型：期刊文献

英文题名：Few-Shot Indoor Localization Model Based on Simplified Graph Convolution and Adversarial Gaussian Process Regression

作者：Li, Xiaonian[1];Guo, Yi[1];Li, Fangfang[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Longdong Univ, Sch Math & Informat Engn, Qingyang 745000, Gansu, Peoples R China

年份：2025

卷号：12

期号：13

起止页码：24490

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：;EI(收录号：20251418162765);WOS:【SCI-EXPANDED(收录号:WOS:001512543400046)】；

基金：This work was supported in part by the Gansu Provincial Science and Technology Plan Project under Grant 23JRZA494, and in part by the Gansu Provincial University Innovation Foundation under Grant 2023A-148.

语种：英文

外文关键词：Fingerprint recognition; Location awareness; Accuracy; Costs; Internet of Things; Data models; Convolution; Green products; Adaptation models; Vectors; Adversarial Gaussian process regression (AGPR); fingerprint-based localization; simplified graph convolutional (SGC); sparse sampling

摘要：Indoor localization technology, a core component of applications such as smart homes and the Internet of Things (IoT), has attracted considerable attention in recent years. Fingerprint-based localization relies on the construction of dense, high-quality fingerprint databases but faces two major challenges: 1) the time-consuming and complex process of collecting received signal strength indicator (RSSI) fingerprint samples and 2) the significant fluctuations in fingerprint data caused by indoor environmental factors, which lead to inaccurate matching. These challenges affect the accuracy, stability, and scalability of localization systems. To address these issues, this article proposes a sparse fingerprint sample collection strategy and introduces a novel few-shot indoor localization model based on a simplified graph convolutional and adversarial Gaussian process regression (SGC-AGPR). The proposed approach reduces the sample collection burden by using sparse sampling, aggregates feature from neighboring nodes through a simplified graph convolutional model (SGC), synthesizes fingerprints for synthesized reference points (SRPs), and incorporates the spatial topological relationships of the reference points (RPs). Additionally, an optimized adversarial Gaussian process regression model (AGPR) is employed to provide initial values for SRP within the graph convolution process, enhancing the statistical correlation between fingerprint samples. Furthermore, this method introduces similarity-based adaptive weights to determine aggregation weights, mitigating issues related to inaccurate fingerprint matching. Experimental results demonstrate that the proposed localization model achieves an accuracy of approximately 0.84 m with limited samples, offering significant improvements in cost, accuracy, and stability compared to traditional methods. The proposed approach provides an innovative solution to the challenges of limited sample availability and environmental interference, contributing to the advancement of indoor localization technology.