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针对基于加权质心算法的井下人员定位方法误差大的问题,提出了一种基于自组织竞争网络的井下人员定位融合算法。该算法利用自组织竞争网络的学习筛选能力,通过分组训练筛选出接近理论值的实际RSSI值,找出用于加权质心算法的有效坐标,在加权质心算法的基础上计算未知节点位置。Matlab仿真结果表明,该算法的定位精度比原加权质心算法显著提高。
Aiming at the problem of large error of location method of underground personnel based on weighted centroid algorithm, this paper proposed a mine personnel positioning fusion algorithm based on self-organizing competition network. The algorithm uses the learning ability of self-organizing competition network to filter out the actual RSSI value close to the theoretical value through packet training, find the effective coordinates for the weighted centroid algorithm, and calculate the unknown node location based on the weighted centroid algorithm. Matlab simulation results show that the proposed algorithm has significantly higher positioning accuracy than the original weighted centroid algorithm.