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煤矿井下无线信道容易受到非视距和多径衰落影响,基于RSSI的定位系统误差较大,研究了一种新的井下定位算法。对卡尔曼滤波算法进行研究,抑制测距误差,建立井下信道模型;提出一种协调器节点选取最优参考节点组的方法,利用信道模型和加权最小二乘法确定移动未知节点位置信息;进一步通过扩展卡尔曼滤波算法求精数据,实现煤矿井下矿工及设备的实时定位。井下巷道实验表明,该算法误差控制在3 m以内,提高了定位精度,增强了定位系统的可靠性,可用于煤矿井下定位。
The wireless channel in coal mine is easy to be affected by non-line-of-sight and multipath fading. The error of positioning system based on RSSI is larger. A new downhole positioning algorithm is studied. The Kalman filtering algorithm is researched to suppress the ranging error and establish the downhole channel model. A method for selecting the optimal reference node group by coordinator nodes is proposed. The channel model and the weighted least square method are used to determine the location information of unknown nodes. Extended Kalman filter algorithm refinement data to achieve real-time positioning of coal miners and equipment. Underground tunnel experiments show that the error of the algorithm is controlled within 3 m, which improves the positioning accuracy and enhances the reliability of the positioning system. It can be used for coal mine positioning.