基于信道状态信息(CSI)的室内定位技术已经被广泛应用于各种场合,在CSI相位指纹定位方案中,采样点密度大小与定位效果好坏紧密相关.由于数据采集工作量的关系,以往学者们多关注低采样密度场景或通过模拟仿真分析采样密度问题,导致难以找到定位误差最小的采样密度.本文设置不同的采样密度,将数据预处理后的CSI相位信息作为指纹,匹配WKNN算法,分析探究了不同采样点密度对于定位精度的影响.实验结果表明,在4 m×4 m的环境中,采样间隔设置为0.4 m,采样密度为7.6个/m2时,定位精度最高,平均误差为0.34 m,同时兼顾采样工作量,具有较高性价比.
Indoor location technology based on channel state information(CSI)has been widely used in various places,in the CSI phase fingerprint location scheme,the density of sampling points is closely related to the location effect.Due to the workload of data collection,previous scholars paid more attention to low-sampling density scenarios or adopted simulation methods to analyze sampling density re-search,which made it difficult to find the sampling density with the smallest positioning error.In this paper,the author first sets different CSI sampling densities,takes the CSI phase after data preprocessing as fingerprint characteristics,and then uses WKNN algorithm to an-alyze and explore the influence of different sampling densities on the positioning effect.The experiment results show that in the environ-ment of 4 m×4 m,when a sampling point is taken every 0.4 m and the sampling density is 7.6 per square meter,the positioning accuracy is the highest,the average error is 0.34 m,and the sampling workload is taken into account.