摘要
针对现有的基于WiFi的位置指纹定位方法精度低、计算复杂度高的问题,提出了一种基于CSI和RSSI的混合位置指纹定位方法MixedFi(A Mixed Fingerprint Localization Method Based on CSI and RSSI)。该方法在离线阶段结合了传统的接收信号强度RSSI(Received Signal Strength Indication)与细粒度的信道状态信息CSI(Channel State Information)并将其作为原始位置指纹,有效利用各个接入点AP(Access Point)的信号特征信息。在线阶段引入空间聚类划分的思想对RSSI指纹地图进行合理划分,降低指纹空间的搜索时间;再通过主成分分析法PCA(Principal Component Analysis)提取CSI指纹特征,最后利用Kendall阶次相关系数自主选择近邻进行加权估计得到最终节点定位结果,解决了传统K近邻KNN(K Nearest Neighbors)方法定位精度低的问题。实验表明,与现有的基于单一指纹的定位方法相比,本文提出的方法有效降低了计算的复杂度,提高了定位精度。
Many existing indoor fingerprint localization methods based on Wi Fi encounter low accuracy and high computational complexity,to address this problem,this paper proposes a mixed fingerprint localization method based on CSI and RSSI. At the off-line stage,the common received signal strength indication and fine-grained channel state information are effectively combined as the raw location fingerprint,which effectively utilizes the signal characteristic information of each access point. At the online stage,the method first introduces the idea of spatial clustering to divide the RSSI fingerprint map reasonably to reduce the search time of fingerprint space. Then the principal component analysis method is used to extract the CSI fingerprint feature. Finally,to solve the problem of low positioning accuracy of traditional KNN method,the Kendall correlation coefficient is used to adaptively select neighbors to estimate the final result. The simulation results show the proposed method effectively reduces the computational complexity,achieve higher localization accuracy than single fingerprinting localization methods.
作者
黑毅力
党小超
郝占军
HEI Yili;DANG Xiaochao;HAO Zhanjun(College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China;Gansu Tianshui No.1 High School,Tianshui Gansu 741000,China;Gansu Province Internet of Things Engineering Research Center,Lanzhou 730070,China)
出处
《传感技术学报》
CAS
CSCD
北大核心
2019年第3期396-404,共9页
Chinese Journal of Sensors and Actuators
基金
国家自然科学基金项目(61363059
61662070)
甘肃省科技重点研发项目(1604FKCA097
17YF1GA015)
