摘要： 基于接收信号强度指示（RSSI）的WiFi指纹定位技术凭借其部署便捷性与成本优势而备受关注。然而，现有指纹定位方法高度依赖于海量数据的模型训练，且数据增强方法生成的虚拟样本数据质量参差不齐，进而影响模型定位性能。此外，模型泛化能力不足，一旦环境发生变化，模型定位性能会急剧下降。为应对以上挑战，本文通过构建深度融合注意力机制的卷积神经网络（CNN）定位模型，结合元学习驱动的小样本学习（FSL），采用粒子群优化（PSO）算法在超参数空间物理约束下进行自动化数据精选与模型协同调优，提出了基于小样本学习驱动的多参数优化WiFi指纹定位方法。实验结果表明，所提方法在CJU数据集和Tampere公开数据集上分别实现了0.52米和6.88米的平均定位误差，相较多种对比算法定位精度分别至少提升了49.5%和8.7%；同时，在CJU-2024数据集上进行了模型泛化性测试，通过少量数据使模型快速适应新环境，所提方法仍能达到2.17米的平均定位误差，定位精度至少提高26.7%。由此表明，所提出的方法能够有效提高室内定位的精度并且能够在新环境下有出色的性能表现，展现出良好的泛化能力。

Abstract: WiFi fingerprint positioning based on received signal strength indication (RSSI) has gained wide attention due to its ease of deployment and cost-effectiveness. However, existing fingerprinting methods typically rely on large-scale training data, while data augmentation often produces virtual samples of uneven quality, thereby limiting positioning accuracy and generalization. To address these issues, this study proposes a multi-parameter optimization WiFi fingerprinting method driven by few-shot learning (FSL). The method integrates an attention-enhanced convolutional neural network (CNN) with a meta-learning framework to enable rapid adaptation under limited data, while particle swarm optimization (PSO) is employed for automated data selection and joint hyperparameter tuning under physical constraints. Experimental results demonstrate that the proposed method achieves average positioning errors of 0.52 m on the CJU dataset and 6.88 m on the public Tampere dataset, improving accuracy by at least 49.5% and 8.7% compared with baseline methods. In addition, a generalization test on the CJU-2024 dataset shows that the model adapts effectively to new environments with only a small amount of data, achieving an average positioning error of 2.17 m and an accuracy improvement of at least 26.7%. These results confirm that the proposed method significantly improves indoor positioning accuracy while maintaining strong generalization capability.