CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach
CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach
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DOI：
10.1109/jiot.2016.2558659
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发表时间：
2016-12-01
影响因子：
10.6
通讯作者：
Mao, Shiwen
中科院分区：
文献类型：
--
作者：
Wang, Xuyu;Gao, Lingjun;Mao, Shiwen
随着定位服务需求的不断增长，基于指纹技术的室内定位由于其精度高、硬件要求低而成为一种越来越重要的技术。在本文中，我们提出了PhaseFi，一个指纹识别系统的室内定位与校准的信道状态信息（CSI）相位信息。在PhaseFi中，首先通过访问修改的设备驱动程序从IEEE 802.11n网络接口卡的多个天线和多个子载波中提取原始相位信息。然后应用线性变换提取校准相位信息，我们证明了有界方差。对于离线阶段，我们设计了一个具有三个隐藏层的深度网络来训练校准的相位数据，并使用深度网络的权重来表示指纹。结合贪婪学习算法逐层训练权重以降低计算复杂度，其中两个连续层之间的子网络形成受限玻尔兹曼机。在在线阶段，我们使用基于径向基函数的概率方法进行在线位置估计。建议的PhaseFi方案的实施和验证与广泛的实验在两个代表性的室内环境。它被证明优于三个基准计划的基础上CSI或接收信号强度在这两种情况下。
With the increasing demand of location-based services, indoor localization based on fingerprinting has become an increasingly important technique due to its high accuracy and low hardware requirement. In this paper, we propose PhaseFi, a fingerprinting system for indoor localization with calibrated channel state information (CSI) phase information. In PhaseFi, the raw phase information is first extracted from the multiple antennas and multiple subcarriers of the IEEE 802.11n network interface card by accessing the modified device driver. Then a linear transformation is applied to extract the calibrated phase information, which we prove to have a bounded variance. For the offline stage, we design a deep network with three hidden layers to train the calibrated phase data, and employ the weights of the deep network to represent fingerprints. A greedy learning algorithm is incorporated to train the weights layer-by-layer to reduce computational complexity, where a subnetwork between two consecutive layers forms a restricted Boltzmann machine. In the online stage, we use a probabilistic method based on the radial basis function for online location estimation. The proposed PhaseFi scheme is implemented and validated with extensive experiments in two representation indoor environments. It is shown to outperform three benchmark schemes based on CSI or received signal strength in both scenarios.