合肥工业大学 计算机与信息学院,安徽,合肥,230009
纸质出版:2018
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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, et al. RSSICorrection Localization Algorithm Based on Environmental Perception[J]. 2018, 35(11): 89-94.
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.
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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韩江洪, 祝满拳, 马学森, 等.基于RSSI的极大似然与加权质心混合定位算法[J].电子测量与仪器学报, 2013, 27(10):937-943. DOI: 937
蔡丽萍, 蒋晶晶, 洪利.一种改进的DV-HOP定位算法[J].微电子学与计算机, 2015, 32(4):120-124. DOI: 120
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