Indoor smartphone positioning is one of the key information and cummunication technology techniques enabling new opportunities for indoor navigation and mobile location-based services to enrich our everyday lives. Generally, the development of an indoor positioning system heavily relies on wireless sensor network. Since wireless sensors can estimate the probable distance between radio source and the sensors themselves by evaluating the strengths of wireless signals received from radio sources, such as received strength signal indications of Wi-Fi and Bluetooth. However, the radio signals could be influenced by indoor and outdoor objects, such as walls and furniture, and carrying mode of a user's smartphone, like in-pocket or in-backpack. But, according to the best of our knowledge, up to present, people do not know how carrying mode information (CMI) influences the positioning accuracy of a positioning system. Therefore, in this study, we propose an indoor positioning scheme, named LEarning-based Indoor Positioning System (LEIPS), which identifies the carrying mode of a user's smartphone by using this smartphone's inertial sensors and deep learning algorithms, aiming to increase indoor positioning accuracy. Our experimental results demonstrate that this system reaches 96% of positioning accuracy. CMI is also validated, showing that it is able to improve indoor prediction accuracy.

中文翻译：

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一种基于深度学习的基于接收强度信号指示和携带方式信息的室内定位方法

室内智能手机定位是关键的信息和通信技术技术之一，为室内导航和移动定位服务提供了新的机会，以丰富我们的日常生活。通常，室内定位系统的开发很大程度上依赖于无线传感器网络。由于无线传感器可以通过评估从无线电源接收到的无线信号的强度（例如 Wi-Fi 和蓝牙的接收强度信号指示）来估计无线电源与传感器本身之间的可能距离。然而，无线电信号可能会受到室内和室外物体（如墙壁和家具）以及用户智能手机的携带方式（如口袋或背包）的影响。但是，据我们所知，到目前为止，人们不知道携带模式信息（CMI）如何影响定位系统的定位精度。因此，在本研究中，我们提出了一种室内定位方案，命名为LE arning基于我ndoor P ositioning小号ystem（LEIPS），其通过使用该智能手机的惯性传感器和深学习算法，目的是增加室内定位精度识别用户的智能电话的携带模式。我们的实验结果表明，该系统达到了 96% 的定位精度。CMI 也得到验证，表明它能够提高室内预测的准确性。