射频识别(radio frequency identification,RFID)技术为工业物联网(industrial internet of things)带来了巨大的进步,作为实现智能仓储的关键技术之一,广泛应用于库存管理和智能定位等场景,然而现有的绝对/相对定位方法易受仓储环境、...射频识别(radio frequency identification,RFID)技术为工业物联网(industrial internet of things)带来了巨大的进步,作为实现智能仓储的关键技术之一,广泛应用于库存管理和智能定位等场景,然而现有的绝对/相对定位方法易受仓储环境、包装材料、货架材质等因素影响。为了进一步提升室内定位精度,该研究提出了一种基于接收信号强度指示器(receive signal strength indicator,RSSI)和测量相位融合的无源RFID定位方法(RFID positioning based on received signal strength indicator and phase measurement,RP-RaP)。首先,使用MATLAB软件进行仿真模拟,在已知测量相位统计学分布的前提下,采用最大似然估计法对标签进行水平定位,同时基于双天线阅读器所测得的RSSI差值对标签进行垂直定位,实现了无源超高频RFID标签的水平和垂直定位仿真。其次,以农产品包装场景为例,在仓库中搭建射频定位测试系统,通过滑轨搭载射频阅读器及天线,对货架物品上的贴附标签进行水平和垂直定位分析,最后将无源标签分别贴附于金属盒、油桶、纸箱、面粉袋和大米袋,并以未贴附标签的测量结果作为对比。试验结果表明,与传统的室内定位算法LANDMARC相比,RP-RaP定位精度明显提升,平均水平和垂直定位精度分别达到94.6%和94.3%,基于接收信号强度指示器和测量相位融合的定位方法有效提升了农产品包装定位精度。研究结果可为大型农产品仓储智能化管理与应用提供参考。展开更多
Existing spatial interpolation methods estimate the property values of an unmeasured point with observations of its closest points based on spatial distance(SD).However,considering that properties of the neighbors spa...Existing spatial interpolation methods estimate the property values of an unmeasured point with observations of its closest points based on spatial distance(SD).However,considering that properties of the neighbors spatially close to the unmeasured point may not be similar,the estimation of properties at the unmeasured one may not be accurate.The present study proposed a local attribute-similarity weighted regression(LASWR)algorithm,which characterized the similarity among spatial points based on non-spatial attributes(NSA)better than on SD.The real soil datasets were used in the validation.Mean absolute error(MAE)and root mean square error(RMSE)were used to compare the performance of LASWR with inverse distance weighting(IDW),ordinary kriging(OK)and geographically weighted regression(GWR).Cross-validation showed that LASWR generally resulted in more accurate predictions than IDW and OK and produced a finer-grained characterization of the spatial relationships between SOC and environmental variables relative to GWR.The present research results suggest that LASWR can play a vital role in improving prediction accuracy and characterizing the influence patterns of environmental variables on response variable.展开更多
文摘射频识别(radio frequency identification,RFID)技术为工业物联网(industrial internet of things)带来了巨大的进步,作为实现智能仓储的关键技术之一,广泛应用于库存管理和智能定位等场景,然而现有的绝对/相对定位方法易受仓储环境、包装材料、货架材质等因素影响。为了进一步提升室内定位精度,该研究提出了一种基于接收信号强度指示器(receive signal strength indicator,RSSI)和测量相位融合的无源RFID定位方法(RFID positioning based on received signal strength indicator and phase measurement,RP-RaP)。首先,使用MATLAB软件进行仿真模拟,在已知测量相位统计学分布的前提下,采用最大似然估计法对标签进行水平定位,同时基于双天线阅读器所测得的RSSI差值对标签进行垂直定位,实现了无源超高频RFID标签的水平和垂直定位仿真。其次,以农产品包装场景为例,在仓库中搭建射频定位测试系统,通过滑轨搭载射频阅读器及天线,对货架物品上的贴附标签进行水平和垂直定位分析,最后将无源标签分别贴附于金属盒、油桶、纸箱、面粉袋和大米袋,并以未贴附标签的测量结果作为对比。试验结果表明,与传统的室内定位算法LANDMARC相比,RP-RaP定位精度明显提升,平均水平和垂直定位精度分别达到94.6%和94.3%,基于接收信号强度指示器和测量相位融合的定位方法有效提升了农产品包装定位精度。研究结果可为大型农产品仓储智能化管理与应用提供参考。
基金supported by National Natural Science Foundation(41201299)the Ministry of Water Resources Public Welfare Industry Scientific Research Special(201501055).
文摘Existing spatial interpolation methods estimate the property values of an unmeasured point with observations of its closest points based on spatial distance(SD).However,considering that properties of the neighbors spatially close to the unmeasured point may not be similar,the estimation of properties at the unmeasured one may not be accurate.The present study proposed a local attribute-similarity weighted regression(LASWR)algorithm,which characterized the similarity among spatial points based on non-spatial attributes(NSA)better than on SD.The real soil datasets were used in the validation.Mean absolute error(MAE)and root mean square error(RMSE)were used to compare the performance of LASWR with inverse distance weighting(IDW),ordinary kriging(OK)and geographically weighted regression(GWR).Cross-validation showed that LASWR generally resulted in more accurate predictions than IDW and OK and produced a finer-grained characterization of the spatial relationships between SOC and environmental variables relative to GWR.The present research results suggest that LASWR can play a vital role in improving prediction accuracy and characterizing the influence patterns of environmental variables on response variable.
