解决手机定位中因设备和时间变化导致的精度下降问题
DAILOC: Domain-Incremental Learning for Indoor Localization using Smartphones
- 用多级变分自编码器分离环境特征与设备差异
- 在多个手机和时间点测试中误差降低2.74倍
- 适合长期部署的智能设备定位系统
基于Wi-Fi指纹的室内定位在实际应用中面临设备差异和环境随时间变化带来的域偏移挑战。现有方法通常分别处理这些问题，导致泛化能力差且易发生灾难性遗忘。本文提出DAILOC，一种联合应对时间与设备相关域偏移的增量学习框架。该方法引入新型解耦策略，利用多级变分自编码器将域偏移与位置相关特征分离；同时提出记忆引导类潜在对齐机制，缓解长期遗忘问题。在多种手机、建筑及时间点上的实验表明，DAILOC显著优于现有最优方法，平均误差降低最多达2.74倍，最坏情况误差降低4.6倍。
原文摘要 · Abstract (English)
Wi-Fi fingerprinting-based indoor localization faces significant challenges in real-world deployments due to domain shifts arising from device heterogeneity and temporal variations within indoor environments. Existing approaches often address these issues independently, resulting in poor generalization and susceptibility to catastrophic forgetting over time. In this work, we propose DAILOC, a novel domain-incremental learning framework that jointly addresses both temporal and device-induced domain shifts. DAILOC introduces a novel disentanglement strategy that separates domain shifts from location-relevant features using a multi-level variational autoencoder. Additionally, we introduce a novel memory-guided class latent alignment mechanism to address the effects of catastrophic forgetting over time. Experiments across multiple smartphones, buildings, and time instances demonstrate that DAILOC significantly outperforms state-of-the-art methods, achieving up to 2.74x lower average error and 4.6x lower worst-case error.
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