智能车辆无线传感定位方法  

Wireless sensor positioning method for intelligent vehicles

作　　者：苟丹丹 张开生[2] 刘振华 GOU Dandan;ZHANG Kaisheng;LIU Zhenhua(School of Electronic Information Engineering,Xi’an Vocational University of Automobile,Xi’an 710600,China;School of Electrical and Control Engineering,Shaanxi University of Science&Technology,Xi’an 710021,China)

机构地区：[1]西安汽车职业大学电子信息工程学院,陕西西安710600 [2]陕西科技大学电气与控制工程学院,陕西西安710021

出　　处：《现代电子技术》2022年第19期31-36,共6页Modern Electronics Technique

基　　金：陕西省科技计划项目(2017GY-063)。

摘　　要：为了实现区域内未知节点定位,减小RSSI接收信号强度稳定性差引起的目标定位误差,提出一种基于物联网的智能车辆无线传感定位方法,建立了带有RSSI引擎的区域传感定位系统。结合智能车辆运动轨迹模型,采用预滤波的序贯质心定位算法进行RSSI测距值校正优化。在运动轨迹测距阶段,将智能车辆信号强度与WSN中基于接收信号强度指示的信标节点间距离同时考虑在质心定位算法内进行RSSI值校正,权值选择阶段出于对智能车辆通信外部环境复杂性的考虑,采用完整的无线信号传播损耗Shadowing模型,基于修正传统权重的路径损失计算方法,进一步修正扩展该模型。通过引入一个服从对数正态分布和零均值高斯分布的随机噪声变量,权值取信标节点距离倒数之和,依据动态参考点群组ZigBee无线传感网络完成辅助定位。经实验验证,加权质心定位算法经权值修正后,充分反映了信标节点影响力,与传统质心定位算法模型相比,RSSI值校正模型在智能车辆无线传感测距和加权定位阶段都有了很大改进,弥补了普通算法利用单节点多跳路由信息交换完成位置估算引起的测距误差。结果表明,此方法具有网络自愈性,RSSI测距误差≤3.5 m,采用多群组动态参考节点定位能够获得较好的定位精度。In order to realize the positioning of unknown nodes in an area and reduce the target positioning error caused by the poor stability of RSSI(received signal strength indication) received signal strength,a wireless sensor positioning method based on the Internet of Things(IoT)is proposed for positioning of intelligent vehicles,and an area sensing positioning system with RSSI engine is established. In combination with the motion trajectory model of the intelligent vehicle,the pre-filtered sequential centroid localization algorithm is used to correct and optimize the RSSI ranging value. In the motion trajectory ranging stage,the signal strength of the intelligent vehicle and the distance between the beacon nodes indicated by the received signal strength in the WSN(wireless sensor network)are also taken into account to correct the RSSI value in the centroid positioning algorithm. In the weight selection stage,a complete wireless signal propagation loss Shadowing model is used because of considering the complexity of external communication environment of the intelligent vehicles,and the model is further modified and extended on the basis of path loss calculation method of modifying the traditional weight. By introducing a random noise variable obeying log-normal distribution and zero-mean Gaussian distribution(the weight is the sum of the distance reciprocal of beacon nodes),the assisted positioning is completed according to the dynamic reference point group ZigBee WSN. It has been verified by experiments that the weighted centroid positioning algorithm can fully reflect the influence of beacon nodes after the weights are corrected. In comparison with the traditional centroid positioning algorithm model,the RSSI value correction model has a great improvement in both the wireless sensor ranging and weighted positioning stages of intelligent vehicles,which makes up for the ranging error caused by the common algorithm using single-node multi-hop routing information exchange to complete the location estimation. The result

关 键 词：无线传感定位 智能车辆 CC2530芯片 ZIGBEE无线传感网络 动态参考点 物联网 Shadowing模型 

分 类 号：TN850.6-34[电子电信—信息与通信工程]