【摘要】 煤矿井下的复杂情况，导致在NLOS环境下，人员定位精度大幅下降，甚至得到的数据无法使用。针对此问题，文章提出一种Chan-PF融合定位算法。首先，使用Chan算法对到达时间差(TDOA)得到的测量值进行处理，获得最优解；其次，采用粒子滤波对最优解再次进行处理，最终粒子重采样后的中心位置即为目标节点的精确位置。仿真结果表明，该算法能有效地抵抗煤矿井下NLOS环境中的干扰，提高定位精度，得到较为精确的定位坐标。更多还原

【Abstract】 Due to the complex situation underground in coal mines, the accuracy of personnel positioning decreases significantly in the NLOS environment, and even the obtained data cannot be used. To address this issue, a Chan-PF fusion positioning algorithm is proposed. Firstly, the Chan algorithm is used to process the measured values of time difference of arrival(TDOA) to obtain the optimal solution. Then, particle filtering is used to process the optimal solution again, and the final center position of the resampled particles is the exact position of the target node. The simulation results show that this algorithm can effectively resist interference in the NLOS environment of coal mines underground, improve positioning accuracy, and obtain more accurate positioning coordinates.更多还原