Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices
Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices
复制标题
DOI：
10.23919/date54114.2022.9774611
复制
发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
Saideep Tiku;S. Pasricha
中科院分区：
文献类型：
--
作者：
Saideep Tiku;S. Pasricha
基于WiFi指纹的智能手机室内定位是一个新兴的应用领域，用于增强室内地点内人员和资产的定位和跟踪。不幸的是，来自独立维护的WiFi接入点(AP)的传输信号特性随时间变化很大。此外，一些在初始部署阶段可见的WiFi AP可能会随着时间的推移而更换或移除。这些因素经常被忽视，并导致部署后室内定位精度的逐渐和灾难性的下降，持续数周和数月。我们提出了一种基于暹罗神经编码器的框架，与该领域的最先进技术相比，随着时间的推移，定位精度的降级最高可降低40%，无需任何重新训练。
WiFi fingerprinting-based indoor localization on smartphones is an emerging application domain for enhanced positioning and tracking of people and assets within indoor locales. Unfortunately, the transmitted signal characteristics from independently maintained WiFi access points (APs) vary greatly over time. Moreover, some of the WiFi APs visible at the initial deployment phase may be replaced or removed over time. These factors are often ignored and cause gradual and catastrophic degradation of indoor localization accuracy post-deployment, over weeks and months. We propose a Siamese neural encoder-based framework that offers up to 40% reduction in degradation of localization accuracy over time compared to the state-of-the-art in the area, without requiring any re-training.