煤矿巷道多源数据融合定位算法研究

Research on multi-source data fusion positioning algorithm for coal mine roadway

  • 摘要: 煤矿巷道非视距条件下单独使用飞行时间定位算法或接收信号强度算法存在方向判断不准确、定位误差较大等问题;飞行时间定位算法与接收信号强度算法联合定位,虽然在方向判断的准确率上较单独的飞行时间定位算法有所提高,但对于标签在非视距运动场景下的判断仍不够准确,定位轨迹不够平滑。针对上述问题,提出了一种基于飞行时间定位算法、接收信号强度及多项式插值方法的煤矿巷道多源数据融合定位算法。该算法首先测量标签与定位基站间的飞行时间,并计算接收信号强度,然后根据标签历史轨迹采用多项式插值方法拟合、预测当前时刻标签位置,根据预测位置、飞行时间和接收信号强度组合判定标签相对基站所在方向,最后通过加权数据融合优化定位结果,从而提高定位稳定性及准确度。实验结果表明,相比于单一飞行时间定位算法或接收信号强度算法,该算法有效提高了定位系统的精度和稳定性,可以更准确地判断标签相对基站的方向,方向判断准确率高达99%。

     

    Abstract: In the non-line-of-sight condition of coal mine roadway, the use of time of flight(TOF) positioning algorithm or received signal strength(RSS) algorithm alone has problems such as inaccurate direction judgement and large positioning errors; TOF positioning algorithm combined with RSS positioning algorithm, although accuracy of direction judgement is improved compared with the single TOF algorithm, the judgement of tags in non-line-of-sight motion scenes is still not accurate enough, and the positioning track is not smooth enough.In view of the above problems, a multi-source data fusion positioning algorithm based on TOF, RSS and polynomial interpolation prediction(PIM) was proposed. The algorithm first measures TOF between label and positioning base station and calculates RSS, then uses PIM to fit and forecast label position at the current moment combined with historical location data, and according to the combination of predicted position, TOF and RSS, the direction of the tag relative to the base station is determined. Finally, the positioning result is optimized by weighted data fusion, so as to improve positioning stability and accuracy. The experimental results show that compared with the single TOF positioning algorithm or RSS algorithm, the proposed algorithm can effectively improve accuracy and stability of the positioning system, and can more accurately determine direction of the tag relative to the base station, and direction determination accuracy is above 99%.

     

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