基于ASGSO-BP的RFID定位方法研究  

Research on RFID Location Method Based on ASGSO-BP

作　　者：卢俊男 肖本贤[1] 倪有源[1] 肖献强 方紫剑 LU Junnan;XIAO Benxian;NI Youyuan;XIAO Xianqiang;FANG Zijian(School of Electrical Engineering and Automation,Hefei University of Technology,Hefei 230009,China;Hefei Banyitong Technology Development Co.,Ltd.,Hefei 230000,China;Hefei Yiheng Intelligent Robot Co.,Ltd.,Hefei 230009,China)

机构地区：[1]合肥工业大学电气与自动化工程学院,安徽合肥230009 [2]合肥搬易通科技发展有限公司,安徽合肥230000 [3]合肥壹恒智能机器人有限公司,安徽合肥230009

出　　处：《仪表技术》2023年第3期69-74,共6页Instrumentation Technology

摘　　要：将自适应步长的萤火虫优化(ASGSO)算法与BP神经网络相结合,形成基于ASGSO-BP的无线信道建模方法。采用数据拟合性能较好的ASGSO-BP网络,构建在室内环境下传输无线信号的路径损耗模型,以提高估算信号传输距离的准确性。利用得到的模型和RFID阅读器检测接收到信号强度值的距离,并将该距离用于LANDMARC算法,进行三维空间的室内定位。仿真实验效果显示,采用该方法可显著提升无线信号室内定位的精度及自适应性,对促进智能网联车辆技术的发展具有重要意义。Combining the Adaptive Step Glowworm Swarm Optimization(ASCSO)algorithm with BP neural network,a wireless channel modeling method based on ASGSO-BP is formed.The ASGSO-BP network with good data fit-ting performance is used to build a path loss model for wireless signal transmission in indoor environment to improve the accuracy of estimating the signal transmission distance.The model and RFID reader are used to detect the distance from the received signal strength value,and the distance is used in the LANDMARC algorithm for indoor three-dimensional spacelocating.The simulation results show that this method can significantly improve the accuracy and adaptability of wireless signal indoor locating.It is of great significance to promote the development of intelligent network connected vehicle technology.

关 键 词：射频识别 定位方法 3D-LANDMARC算法 ASGSO算法 BP神经网络 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN911.73[自动化与计算机技术—控制科学与工程]