Dang Xiaochao, Tang Xuhao, Hao Zhanjun, Liu Yang
College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China.
Gansu Internet of Things Engineering Research Center, Lanzhou 730070, China.
Sensors (Basel). 2019 Jul 23;19(14):3233. doi: 10.3390/s19143233.
Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.
在无线通信技术不断加速发展的背景下,我们对基于位置的服务要求越来越高;因此,被动室内定位受到了广泛关注。信道状态信息(CSI)由于能够提供更详细和细粒度的信息,受到了研究人员的关注。然而,现有的室内定位方法容易受到环境影响,由于指纹精度有限,无法充分反映所有位置特征。作为一种解决方案,提出了一种基于CSI的被动室内定位方法,采用小波域去噪(WDD)处理采集到的CSI幅度,并在离线阶段对CSI相位信息进行解缠和线性变换。将处理后的幅度和相位作为指纹数据构建指纹数据库,并与参考点位置信息相关联。在两种不同环境下对实验数据分析的结果表明,该方法与同类方法相比具有更低的定位误差和更高的稳定性,能够提供分米级的定位精度。