Indoor Fingerprinting With Bimodal CSI Tensors: A Deep Residual Sharing Learning Approach
Indoor Fingerprinting With Bimodal CSI Tensors: A Deep Residual Sharing Learning Approach
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DOI：
10.1109/jiot.2020.3026608
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发表时间：
2021-03
影响因子：
10.6
通讯作者：
Xiangyu Wang;Xuyu Wang;S. Mao
中科院分区：
文献类型：
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作者：
Xiangyu Wang;Xuyu Wang;S. Mao
基于wi - fi的室内指纹识别由于在室内环境中无处不在而引起了研究界越来越多的兴趣。在本文中，我们提出了一种基于深度残差共享学习的室内指纹识别系统ResLoc，该系统使用双峰通道状态信息（CSI）张量数据。本文提出的ResLoc系统利用一小部分已知坐标的训练点收集的CSI张量数据（包括到达角度和振幅）来训练所提出的双通道深度残差共享学习模型。该模型对传统的深度残差学习模型进行了扩展，将两个或多个信道合并，并在每个残差块之后让信道交换残差信号。与之前基于深度学习的指纹识别方案不同，ResLoc只需要为所有训练位置训练一组权重。提出的ResLoc系统在商用Wi-Fi设备上实现，并在三个代表性的室内环境中进行了广泛的实验评估。实验结果验证了该系统在室内环境下使用单个Wi-Fi接入点即可实现较高的定位精度。
Wi-Fi-based indoor fingerprinting is attracting increasing interest in the research community due to the ubiquitous access in indoor environments. In this article, we propose ResLoc, a deep residual sharing learning-based system for indoor fingerprinting using bimodal channel state information (CSI) tensor data. The proposed ResLoc system employs CSI tensor data, including the angle of arrival and amplitude, collected from a small set of training locations with known coordinates to train the proposed dual-channel deep residual sharing learning model. The proposed new model extends the traditional deep residual learning model by incorporating two or more channels and let the channels exchange their residual signals after each residual block. Unlike prior deep-learning-based fingerprinting schemes, ResLoc only requires for training one group of weights for all the training locations. The proposed ResLoc system is implemented with commodity Wi-Fi devices and evaluated with extensive experiments in three representative indoor environments. The experimental results validate that the proposed ResLoc system can achieve high localization accuracy using a single Wi-Fi access point in indoor environments.