检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
机构地区:[1]公安部第一研究所,公安部警务物联网应用技术重点实验室,北京100048
出 处:《传感技术学报》2016年第4期601-605,共5页Chinese Journal of Sensors and Actuators
摘 要:狭长空间定位问题普遍存在于室内定位应用场景中,虽然传统基于RSSI(Received Signal Strength Indicator)测距的定位方法简便易行,但是狭长空间RSS的波动性以及人体对无线信号的遮挡会严重降低人员定位精度。本文在分析了人体穿透损耗对狭长空间定位影响的基础上,提出将RSSI测距与扩展卡尔曼滤波定位算法组合实现定位,即在中等尺度(5λ-50λ)内采用基于人体穿透损耗模型的RSSI测距方法定位,在大尺度(〉50λ)内采用基于人体遮挡修正模型的扩展卡尔曼滤波算法定位。实验表明该方法在狭长空间的定位精度明显优于RSSI测距定位方法。Long and narrow space positioning problems are wide spread in the indoor positioning applications,thetradition almethod based on RSSI(Received Signal Strength Indicator)ranging is simple,but in long and narrowspace,the RSS volatility and the shade of human body on wirelesss ignalcan seriously reduce the positioning accura-cy. In this paper,the effect of human body shadow loss of long and narrow spaceis first analyzed,than combinationpositioning method is proposed,which skill fully combined RSSI ranging with extended Kalman filter positioning al-gorithm. That is,in the medium scale(5λ-50λ)using RSSI ranging method based on the human body penetrationloss model,in large scale(〉50λ)using extended Kalman filtering algorithm based on the human body penetrationamendment model. Experiments show that the positioning accuracy of the method in the longand narrow spaceis ob-viously better than the RSSI ranging positioning method.
分 类 号:TP393[自动化与计算机技术—计算机应用技术]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.249