摘要
传统室内人员行为检测方法检测准确率较低,稳定性较差。为此,提出一种基于信道状态信息(CSI)的室内人员行为检测方法。采集CSI原始数据包后使用Kalman滤波算法对其进行过滤,运用SVM算法对过滤后的数据作分类处理并建立指纹库。同时,利用PSO算法修正SVM中的参数,然后采用SVM算法处理从真实环境内实时采集到的数据后,将该实时数据与指纹库的数据一一匹配。在此基础上,实现室内人员的行为检测。实验结果表明,相比LIFS、FIMD方法,该方法可以更精细地识别室内人员的动作行为,且稳定性更高。
The traditional indoor personnel behavior detection method has lowaccuracy and poor stability. To solve this problem,an indoor personnel behavior detection method based on Channel State Information（CSI） is proposed. After collecting the CSI raw data package,it uses Kalman filtering algorithm to filter it,and uses the SVM algorithm to classify the filtered data,and then builds the fingerprint database. At the same time,the PSO algorithm is used to modify the parameters of the SVM,then the real time data collected from the real environment is processed by SVM,and the realtime data is matched with the data of the fingerprint library. On this basis,indoor personnel behavior detection is realized.Experimental results show that,compared with LIFS and FIMD method,this method can identify the behavior of indoor personnel more accurately and has higher stability.
作者
党小超
黄亚宁
郝占军
司雄
DANG Xiaochao1,2, HUANG Yaning1, HAO Zhanjun1,2, SI Xiong1(1. College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070, China;2. Gansu Province Internet of Things Engineering Research Center, Lanzhou 730070, Chin)
出处
《计算机工程》
CAS
CSCD
北大核心
2018年第8期79-85,共7页
Computer Engineering
基金
国家自然科学基金(61762079
61363059
61662070)
甘肃省科技重点研发项目(1604FKCA097
17YF1GA015)
甘肃省科技创新项目(17CX2JA037
17CX2JA039)
关键词
行为检测
信道状态信息
支持向量机
粒子群算法
卡尔曼滤波
behavior detection
Channel State Information （CSI）
Support Vector Machine （SVM）
Particle Swarm Optimization （ PSO ）
Kalman filtering
