Indoor positioning system plays a key role in location-based services since the widely used global navigation satellite system (GNSS) is denied in indoor scenarios. Crowdsensing or walking-surveying-based indoor positioning is proposed aiming at providing low-cost and high-efficient 3-D location. This article proposes a crowdsensing/walking-surveying 3-D indoor positioning system by fusing the crowdsensed inertial data and Wi-Fi fingerprinting samples using deep learning frameworks. A sine-wave-based step detector is used for pedestrian dead-reckoning (PDR) to generate original dense-trajectories. An enhanced optimization-based algorithm (Opt) and a smoothing-based algorithm (Smo) are proposed and evaluated to correct the original dense-trajectories into near-true dense-trajectories which are used to construct the inertial database and Wi-Fi radio map. A ResNet-based inertial neural network and a BiLSTM-based Wi-Fi fingerprinting neural network are trained on the constructed navigation database and combined by a Kalman filter to provide accurate and robust 3-D localization performance. The realistic experimental results among complex indoor environments demonstrate that the proposed algorithms are proved to achieve a precise 3-D indoor localization performance which is superior to several existing relative methods.

中文翻译：

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CWIWD-IPS：群体感应/步行测量惯性/Wi-Fi 数据驱动的室内定位系统


由于广泛使用的全球导航卫星系统（GNSS）无法在室内场景中使用，室内定位系统在基于位置的服务中发挥着关键作用。提出基于人群感知或步行测量的室内定位，旨在提供低成本、高效的 3D 定位。本文提出了一种利用深度学习框架融合众感惯性数据和 Wi-Fi 指纹样本的众感/步行测量 3D 室内定位系统。基于正弦波的步进检测器用于行人航位推算 (PDR)，以生成原始的密集轨迹。提出并评估了基于增强优化的算法（Opt）和基于平滑的算法（Smo），以将原始密集轨迹校正为接近真实的密集轨迹，用于构建惯性数据库和Wi-Fi无线电地图。基于 ResNet 的惯性神经网络和基于 BiLSTM 的 Wi-Fi 指纹神经网络在构建的导航数据库上进行训练，并通过卡尔曼滤波器组合，以提供准确且稳健的 3D 定位性能。复杂室内环境中的真实实验结果表明，所提出的算法能够实现精确的3D室内定位性能，优于现有的几种相关方法。