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.

STELLAR是一种新颖的框架，采用对比学习方法，利用Siamese多头注意力神经网络来综合地解决设备异构性和时间变化的挑战，无需重新训练模型（无需重新校准），在不同的室内环境中的评估表明，相比于最先进的技术，其准确性提高了8-75%，有效地解决了设备异构性的挑战。此外，STELLAR表现出18-165%的优势，能够应对长达2年的时间变化，展示了其鲁棒性和适应性。

STELLAR：用于室内定位的连体多头注意力神经网络，克服时间变化和设备异质性