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

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.