【摘要】 基于无线射频识别(RFID)技术密集无源标签定位系统的定位算法大多存在标签性能差异、天线方向性未定和外在环境噪声干扰等问题,导致系统定位精度大幅降低。为提高定位精度,提出一种被动式RFID二维室内定位算法IPABACM。该算法通过部署标签阵列使RFID阅读器读取信息,将读取到的标签赋予权值,利用改变标签权重并用神经网络进行训练的方法降低环境噪声和标签性能差异的影响,根据分析天线覆盖模型得出,通过旋转叠加读写器覆盖区域,可降低甚至消除读写器天线方向对定位精度造成的影响。实验结果表明,与传统的被动式RFID定位算法相比,IPABACM算法具有较高的定位精度,且定位时间较短。更多还原

【Abstract】 Current positioning algorithms based on Radio Frequency Identification(RFID) location systems with dense passive tags face the issue of low localization precision due to the variation of behavior of tags,the ignorance of antenna directivity,external environment noise interference.To solve these problems and improve the positioning accuracy,the paper proposes a 2D passive RFID indoor positioning algorithm(IPABACM) by taking the directionality of the antenna into account in dense passive RFID tag distribution applications.Considering the effects of noise and tag performance differences in the production,the readied tags are assigned weights and trained through neural network.The deviation caused by antenna pattern’s uncertainty is eliminated by superimposing the antenna coverage area as a more regular pattern,which significantly improves the positioning accuracy.Experimental results show that compared with the traditional passive RFID localization algorithm,the IPABACM algorithm can provide relatively better accuracy and less positioning time.更多还原