节点所处的坐标位置信息在无线传感器网络的实际应用中必不可少.本文在传统接收信号强度指示(Received Signal Strength Indication,RSSI)定位算法基础上,为了进一步提高未知节点的定位精密度,提出一种基于环境感知的RSSI校正定位算法....节点所处的坐标位置信息在无线传感器网络的实际应用中必不可少.本文在传统接收信号强度指示(Received Signal Strength Indication,RSSI)定位算法基础上,为了进一步提高未知节点的定位精密度,提出一种基于环境感知的RSSI校正定位算法.算法先对RSSI数据使用高斯过滤,减少RSSI测量偏差;其次结合RSSI计算当前路径损耗指数,实现环境感知;接着测量节点间距离,再用比例关系校正测量结果,进一步减弱环境因素对定位的影响;然后生成信标节点对未知节点定位影响的加权系数;最后通过最小二乘法及带加权系数的质心计算公式来得出节点的最终位置坐标.仿真实验结果显示,算法的定位精度有明显的提高,与实际值的误差在1m左右.展开更多
In recent years, we have been able to use various services using the position information of smartphones and tablets. In addition, research on intelligent transport systems (ITS) has been actively conducted. To consid...In recent years, we have been able to use various services using the position information of smartphones and tablets. In addition, research on intelligent transport systems (ITS) has been actively conducted. To consider reducing traffic accidents by exchanging position information between pedestrians and vehicles by vehicle-to-pedestrian communication, we require accurate position information for pedestrians and vehicles. The GPS (global positioning system) is the most widely used method for acquiring position information. However, in urban areas, the GPS signal is affected by the surrounding buildings, which increases the positioning error. In this study, a method to improve the positioning accuracy of pedestrians using the signal strengths from vehicles and beacons was proposed. First, a Kalman filter was applied to the signal strength. Then, the path loss index was dynamically calculated using vehicle-to-vehicle communication. Finally, the position of a pedestrian was obtained using weighted centroid localization (WCL) after filtering the nodes. The positioning accuracy was evaluated using a simulator and demonstrated the superiority of the proposed method.展开更多
文摘节点所处的坐标位置信息在无线传感器网络的实际应用中必不可少.本文在传统接收信号强度指示(Received Signal Strength Indication,RSSI)定位算法基础上,为了进一步提高未知节点的定位精密度,提出一种基于环境感知的RSSI校正定位算法.算法先对RSSI数据使用高斯过滤,减少RSSI测量偏差;其次结合RSSI计算当前路径损耗指数,实现环境感知;接着测量节点间距离,再用比例关系校正测量结果,进一步减弱环境因素对定位的影响;然后生成信标节点对未知节点定位影响的加权系数;最后通过最小二乘法及带加权系数的质心计算公式来得出节点的最终位置坐标.仿真实验结果显示,算法的定位精度有明显的提高,与实际值的误差在1m左右.
文摘In recent years, we have been able to use various services using the position information of smartphones and tablets. In addition, research on intelligent transport systems (ITS) has been actively conducted. To consider reducing traffic accidents by exchanging position information between pedestrians and vehicles by vehicle-to-pedestrian communication, we require accurate position information for pedestrians and vehicles. The GPS (global positioning system) is the most widely used method for acquiring position information. However, in urban areas, the GPS signal is affected by the surrounding buildings, which increases the positioning error. In this study, a method to improve the positioning accuracy of pedestrians using the signal strengths from vehicles and beacons was proposed. First, a Kalman filter was applied to the signal strength. Then, the path loss index was dynamically calculated using vehicle-to-vehicle communication. Finally, the position of a pedestrian was obtained using weighted centroid localization (WCL) after filtering the nodes. The positioning accuracy was evaluated using a simulator and demonstrated the superiority of the proposed method.
