煤矿井下区间分段视距节点合作定位算法

Algorithm for cooperational localization of the sectional interval and LOS node in a coal mine

针对煤矿井下长距离定位时节点信号波动大、非视距路径信号衰减严重造成定位精度低问题,提出一种区间分段式视距节点合作定位算法。该算法利用学习向量量化聚类将长距离信号传输区间自定义分段,利用分段阈值选择未知节点所属区间;把已定位出结果的未知节点视为其他未知节点的虚拟参考节点,实现所有节点信息相互交流,在节点筛选思想下,利用信道状态信息,克服多径效应来寻找视距路径节点,将近距离区间内的已定位视距路径节点代替远距离区间内的参考节点,减少远距离参考节点的使用。结果表明,与传统未分段、未寻找视距路径节点合作的算法相比,定位误差只有1.5m,精度提高率达到85%。

To overcome the problem of serious signal fluctuation and Non-line-of-sight signal attenuation with long distance positioning in a coal mine,a segmentation node cooperative localization algorithm is proposed.By using the learning vector quantization clustering to segment the long distance transmission interval,the threshold is used to select the range for an unknown node.We think of the unknown node that has been located as the virtual reference node of other unknown nodes,so that all node information can communicate with each other.In the idea of node screening,the multi-path effects are overcome and line-of-sight nodes are searched by channel state information.The reference nodes in the long range are replaced by the optimally located line-of-sight nodes in the close range.Results show that compared with the traditional unsegmented and unfinished line-of-sight path nodes,the positioning error is only 1.5m and the accuracy improvement rate is 85%.