With the increasing demand for location-based services, indoor fingerprinting localization based on received signal strength indicator or channel state information (CSI) has become an increasingly important technique due to its low hardware requirement and high accuracy. Due to robustness against the multipath effect, frequency domain CSI of orthogonal frequency division multiplexing systems is supposed to provide an excellent positioning measurement for indoor localization. In this paper, we propose a novel fingerprint localization method based on modified CSI using the Kalman Filter. For the offline stage, we use modified CSI to build a fingerprint database. In the online stage, we employ the K-nearest neighbor method for location estimation. The proposed indoor fingerprint localization scheme is implemented and validated with experiments in a representative indoor environment with commercial IEEE 802.11 NICs. Compared with existing methods, the experimental results demonstrate that the proposed method can effectively reduce positioning error.

中文翻译：

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一种基于卡尔曼滤波器修正信道状态信息的指纹定位新算法

随着对基于位置的服务需求的增加，基于接收信号强度指标或信道状态信息（CSI）的室内指纹定位由于其硬件要求低和精度高而成为越来越重要的技术。由于对多径效应的鲁棒性，正交频分复用系统的频域 CSI 应该为室内定位提供出色的定位测量。在本文中，我们提出了一种使用卡尔曼滤波器基于修改后的 CSI 的新型指纹定位方法。对于离线阶段，我们使用修改后的 CSI 来构建指纹数据库。在在线阶段，我们采用 K-最近邻方法进行位置估计。提议的室内指纹定位方案在具有商业 IEEE 802.11 NIC 的代表性室内环境中通过实验实施和验证。与现有方法相比，实验结果表明，该方法可以有效降低定位误差。