论文标题
部分可观测时空混沌系统的无模型预测
Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization
论文作者
论文摘要
智能手机与RSSI指纹识别一起是一种有效的方法，可提供低成本和高智能的室内定位解决方案。但是，一些关键的挑战阻止了该技术在公共领域的广泛扩散。一个关键的挑战是设备异质性，即，在不同智能手机设备上捕获的RSSI信号特性的变化。在现实世界中，用于捕获RSSI指纹的智能手机或IoT设备通常在室内本地化服务的用户中有所不同。常规的室内定位解决方案可能无法应对设备引起的变化，从而降低其本地化精度。我们提出了一个多头注意神经网络基于室内的室内定位框架，该框架对设备异质性具有弹性。与最先进的室内定位技术相比，对我们提出的框架的深入分析表明，精度提高了35％。
Smartphones together with RSSI fingerprinting serve as an efficient approach for delivering a low-cost and high-accuracy indoor localization solution. However, a few critical challenges have prevented the wide-spread proliferation of this technology in the public domain. One such critical challenge is device heterogeneity, i.e., the variation in the RSSI signal characteristics captured across different smartphone devices. In the real-world, the smartphones or IoT devices used to capture RSSI fingerprints typically vary across users of an indoor localization service. Conventional indoor localization solutions may not be able to cope with device-induced variations which can degrade their localization accuracy. We propose a multi-head attention neural network-based indoor localization framework that is resilient to device heterogeneity. An in-depth analysis of our proposed framework across a variety of indoor environments demonstrates up to 35% accuracy improvement compared to state-of-the-art indoor localization techniques.
