煤矿井下人员超宽带定位的联合解算方法

Hybrid solution method for ultra-wideband positioning of mine personnel

为构建高精度的实时定位系统,提升煤矿智能化水平,基于超宽带UWB技术,提出一种联合自适应蚁群算法和Taylor级数展开算法的煤矿井下定位算法。利用自适应蚁群算法优化待测点的坐标值,与Taylor级数展开算法相结合,找到更为准确的目标位置,弥补Taylor级数展开算法因初值不准确而导致定位精度下降的问题,提高超宽带井下人员定位系统的精度。实验表明,文中提出的方法在位置解的准确性上优于现有的Chan和Chan-Taylor等算法,定位精度在12 cm之内,能够较好地满足煤矿井下人员精确定位要求。

This paper proposes a coal mine underground positioning algorithm combining adaptive ant colony algorithm(ACO)and Taylor level expansion algorithm based on ultra-wideband UWB technology to develop a high-precision real-time positioning system and accelerate the improvement of coal mine intelligence.The study is enabled by using adaptive ant colony algorithm to optimize the coordinates of the point to be measured;combining with the Taylor level expansion algorithm to find a more accurate target location,which compensates the problem of decrease of the positioning accuracy caused by the inaccuracy of the initial value of the Taylor level expansion algorithm,thus improving the accuracy of UWB underground personnel positioning system.Simulation experiments show that the proposed method is better than the existing Chan and Chan-Taylor algorithms in terms of the accuracy of the position solution,and the positioning accuracy is within 12 cm,which can better meet the requirements of precise positioning in the pit.