基于相控阵的WiFi CSI定位系统的性能预测

Performance Prediction for WiFi CSI Localization System Based on Phased Array

WiFi作为当前最重要的通信方式之一,基于WiFi信号的室内定位系统最有望在日常生活中得到广泛地部署应用.最新研究表明,当采用WiFi通信过程中获取的信道状态信息(CSI)对目标进行定位时,系统可实现亚米级的定位精度.然而,实验场景下的定位精度受到测试样点位置、WiFi设备布局、天线布局等诸多因素的影响.因为目前仍缺少WiFi CSI定位性能预测方法,WiFi定位系统部署后往往难以获得预期的精度.为此,面向多样化场景提出WiFi CSI定位性能的预测模型.首先,从CSI定位的基本物理模型出发,定义天线对的误差微元函数,并通过对定位空间的分析生成误差微元矩阵以及定位性能热度图;其次,对天线对进行拓展,通过引入多天线融合方法、多设备融合方法构建通用的CSI定位性能预测模型;最后,为了将真实场景地图考虑在内,提出将上述热度图与场景地图相融合的方法,从而实现场景定制化的性能预测.在理论分析的基础上,结合2个不同场景下的实验数据验证了定位性能预测模型有效性.实验结果表明,实际定位精度的变化趋势与理论模型相吻合,通过理论模型分析可将定位精度优化32%–37%.

WiFi is one of the most important communication modes at present,and indoor localization systems based on WiFi signals are most promising for widespread deployment and application in daily life.The latest research shows that such a system can achieve submeter-level localization accuracy when it utilizes the channel state information(CSI)obtained during WiFi communication for target localization.However,the accuracy of localization in experimental scenarios depends on many factors,such as the location of the test points,the layout of the WiFi devices,and that of the antennas.Moreover,the WiFi localization systems deployed often fail to provide the desired accuracy since performance prediction methods for WiFi CSI localization are still unavailable.For the above reasons,this study develops a performance prediction model for WiFi CSI localization that applies to diverse scenarios.Specifically,the study defines the error infinitesimal function between a pair of antennas on the basis of the basic physical CSI localization model.The error infinitesimal matrix and the corresponding heat map of localization performance are generated by analyzing the localization space.Then,multi-antenna fusion and multi-device fusion methods are adopted to extend the antenna pairs,thereby constructing a general performance prediction model for CSI localization.Finally,the study proposes integrating the abovementioned heat map with scenario maps to give due consideration to actual scenario maps and ultimately provide a customized performance prediction solution for a given scenario.In addition to the theoretical analysis,this study verifies the effectiveness of the proposed performance prediction model for localization with experimental data in two scenarios.The experimental results show that the actual localization accuracy is consistent with the proposed theoretical model in variation trend,and the model optimizes the localization accuracy by 32%–37%.