狭长空间基于人体穿透损耗模型的组合定位方法

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。

Combination positioning method for long and narrow space based on the human body penetration loss model

Long and narrow space positioning problems are wide spread in the indoor positioning applications,the tradition almethod based on RSSI(Received Signal Strength Indicator)ranging is simple,but in long and narrow space,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 combination positioning 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 penetration loss model,in large scale(>50λ)using extended Kalman filtering algorithm based on the human body penetration amendment model. Experiments show that the positioning accuracy of the method in the longand narrow spaceis ob?viously better than the RSSI ranging positioning method.