马学森, 宫帅, 朱建, 谈杰. 基于环境感知的RSSI校正定位算法[J]. 微电子学与计算机, 2018, 35(11): 89-94.
引用本文: 马学森, 宫帅, 朱建, 谈杰. 基于环境感知的RSSI校正定位算法[J]. 微电子学与计算机, 2018, 35(11): 89-94.
MA Xu-sen, GONG Shuai, ZHU Jian, TAN Jie. RSSICorrection Localization Algorithm Based on Environmental Perception[J]. Microelectronics & Computer, 2018, 35(11): 89-94.
Citation: MA Xu-sen, GONG Shuai, ZHU Jian, TAN Jie. RSSICorrection Localization Algorithm Based on Environmental Perception[J]. Microelectronics & Computer, 2018, 35(11): 89-94.

基于环境感知的RSSI校正定位算法

RSSICorrection Localization Algorithm Based on Environmental Perception

  • 摘要: 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.

     

    Abstract: The coordinate position information of the node is indispensable in the practical application of wireless sensor network. In this paper, a RSSI correction localization algorithm based on environmental perception is proposed, on the basis of the traditional received signal strength indication (RSSI) location algorithm, with the aim of improving location accuracy of nodes. Firstly, the RSSI data is filtered by Gauss to reduce the RSSI measurement bias. Secondly, the path loss index is calculated with RSSI to achieve environmental perception. Thirdly, the distances between the nodes are measured, and then the measurement results are calibrated by the proportional relationship to further reduce the impact of environmental factors on the ranging. Subsequently, the weighting coefficients that beacon nodes disturb unknown nodes are generated. Finally, the final position coordinates of the node are obtained by the least square method and the centroid formula with the weighted coefficient. The simulation results show that the positioning accuracy of the algorithm is obviously improved, with the actual value of the error of about 1m.

     

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