STELLAR：用于克服室内定位中时间变化与设备异构性的孪生多头注意力神经网络
STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization
AI总结：
针对室内定位中设备异构性和时间变化的痛点，提出基于孪生多头注意力神经网络的免重训练对比学习框架STELLAR，在两类场景下精度均显著优于现有方法。
摘要：
基于智能手机的室内定位已成为一种兼具成本效益与准确性的室内移动及IoT设备定位方案。然而，设备异构性和时间变化的挑战阻碍了其广泛应用与精度提升。为全面协同解决这些挑战，我们提出STELLAR——一种采用对比学习方法、基于孪生多头注意力神经网络的新型框架。STELLAR是首个无需重新训练模型（免重新校准）即可同时应对室内定位中设备异构性与时间变化问题的方案。我们在多种室内环境下的评估显示，针对设备异构性挑战，其精度较现有最优技术提升8%-75%；此外，在跨越2年的时间变化场景下，STELLAR性能优于现有方法18%-165%，展现出强大的鲁棒性与适应性。
英文摘要：
Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy. Towards jointly addressing these challenges comprehensively, we propose STELLAR, a novel framework implementing a contrastive learning approach that leverages a Siamese multi-headed attention neural network. STELLAR is the first solution that simultaneously tackles device heterogeneity and temporal variations in indoor localization, without the need for retraining the model (re-calibration-free). Our evaluations across diverse indoor environments show 8-75% improvements in accuracy compared to state-of-the-art techniques, to effectively address the device heterogeneity challenge. Moreover, STELLAR outperforms existing methods by 18-165% over 2 years of temporal variations, showcasing its robustness and adaptability.
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