Mine weighted centroid positioning algorithm based on improved Gaussian mixture filter
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摘要: 针对煤矿井下复杂环境中无线信号的非视距传播导致RSSI定位算法存在测距误差大及定位结果不准确的问题，提出了一种基于改进高斯混合滤波的矿井加权质心定位算法。首先根据最大期望算法对未知节点的相应RSSI测量数据进行聚类，将它们划分为多个高斯概率密度函数模型；然后根据数据特征，利用赤池信息量准则对采样数据进行优化，得到精确的测量值；最后计算未知节点的初始坐标，将未知节点初始坐标和真实坐标间的误差值作为权值因子，结合质心定位算法计算得到未知节点的最终坐标，实现目标定位。仿真与实验结果表明，该定位算法可实现煤矿井下人员的高精度定位，平均定位误差为1.83 m。Abstract: In view of the problems that RSSI positioning algorithm has large ranging error and inaccurate positioning result due to non-line-of-sight propagation of wireless signals in complex environment of underground coal mine, a mine weighted centroid positioning algorithm based on improved Gaussian mixture filter was proposed. Firstly, corresponding RSSI measurement data of unknown nodes are clustered according to the maximum expectation algorithm, and the data are divided into multiple Gaussian probability density functions models. Then, according to the characteristics of the data, the sampling data is optimized by using Akaike information criterion, and the accurate measured values are obtained. Finally, the initial coordinates of the unknown node are calculated, the error between the initial coordinates of the unknown node and the real coordinates are used as the weight factor, and the final coordinates of the unknown node are calculated combining with the centroid positioning algorithm, so as to realize target positioning. The simulation and experimental results show that the positioning algorithm can realize high precision personnel positioning in underground coal mine, and the average positioning error is 1.83 m.
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