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.
翻译:智能手机加上简易新闻聚合指纹是提供低成本和高准确度室内本地化解决方案的有效方法,然而,一些关键挑战阻止了这一技术在公共领域的广泛扩散。其中一项关键挑战就是装置的异质性,即不同智能设备所捕捉到的简易新闻聚合信号特征的变异性。在现实世界中,用于获取简易新闻聚合指纹的智能手机或IoT装置通常在室内本地化服务的用户中各异。传统的室内本地化解决方案可能无法应对设备引起的变异,这些变异可能降低其本地化的准确性。我们提议了一个多头神经网络的室内本地化框架,以适应装置异质性。对各种室内环境的拟议框架的深入分析表明,与最先进的室内本地本地化技术相比,准确率提高了35%。