School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China.
Sensors (Basel). 2019 Nov 3;19(21):4783. doi: 10.3390/s19214783.
Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)-a kind of information collected in the physical layer-is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback-Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach.
最近，人们对无线传感应用越来越感兴趣，其中室内定位是最具吸引力的应用之一。一般来说，室内定位可以分为基于设备的定位和无设备定位（DFL）。前者需要目标携带某些设备或传感器来辅助定位过程，而后者则没有这样的要求，只需要在环境周围部署无线网络来感知目标，这使得定位更加具有挑战性。信道状态信息（CSI）——一种在物理层采集的信息——由多个子载波组成，具有高度细化的粒度，已逐渐成为室内定位应用的焦点。在本文中，我们提出了一种通过利用 CSI 的不确定性来执行 DFL 任务的方法。我们分别利用多个通信链路的 CSI 幅度和相位来构建指纹，每个指纹都是一组多元高斯分布，反映了 CSI 的不确定性信息。此外，我们提出了一种组合指纹，同时利用 CSI 幅度和相位，希望提高定位精度。然后，我们采用基于 Kullback-Leibler 散度（KL 散度）的核函数来计算测试指纹属于所有参考位置的概率。接下来，为了对目标进行定位，我们将计算出的概率作为权重来平均参考位置。实验结果表明，无论使用哪种类型的指纹，所提出的方法在两个典型的室内环境中都优于现有的 Pilot 和 Nuzzer 系统。我们进行了广泛的实验来探索不同参数对定位性能的影响，结果表明了所提出方法的有效性。
